refactor(backend): extract _extract_agent_json helper, fail fast on unknown poll status

Platform service now submits jobs to agent-generator and polls for results
(10s interval) instead of blocking on a single HTTP call for up to 30 min.
asyncio.sleep in the poll loop yields to the event loop, keeping SSE
heartbeats alive through GCP's L7 load balancer.

Frontend auto-reconnects up to 3 times when SSE drops mid-stream.

autogpt_platform/backend/.env.default

@@ -190,5 +190,8 @@ ZEROBOUNCE_API_KEY=
 POSTHOG_API_KEY=
 POSTHOG_HOST=https://eu.i.posthog.com
 
+# Tally Form Integration (pre-populate business understanding on signup)
+TALLY_API_KEY=
+
 # Other Services
 AUTOMOD_API_KEY=

autogpt_platform/backend/backend/api/features/chat/routes.py

@@ -2,23 +2,19 @@
 
 import asyncio
 import logging
-import uuid as uuid_module
 from collections.abc import AsyncGenerator
 from typing import Annotated
+from uuid import uuid4
 
 from autogpt_libs import auth
-from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response, Security
+from fastapi import APIRouter, Depends, HTTPException, Query, Response, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
 from backend.copilot import service as chat_service
 from backend.copilot import stream_registry
-from backend.copilot.completion_handler import (
-    process_operation_failure,
-    process_operation_success,
-)
 from backend.copilot.config import ChatConfig
-from backend.copilot.executor.utils import enqueue_cancel_task, enqueue_copilot_task
+from backend.copilot.executor.utils import enqueue_cancel_task, enqueue_copilot_turn
 from backend.copilot.model import (
     ChatMessage,
     ChatSession,
@@ -46,9 +42,6 @@ from backend.copilot.tools.models import (
     InputValidationErrorResponse,
     NeedLoginResponse,
     NoResultsResponse,
-    OperationInProgressResponse,
-    OperationPendingResponse,
-    OperationStartedResponse,
     SetupRequirementsResponse,
     SuggestedGoalResponse,
     UnderstandingUpdatedResponse,
@@ -99,10 +92,8 @@ class CreateSessionResponse(BaseModel):
 class ActiveStreamInfo(BaseModel):
     """Information about an active stream for reconnection."""
 
-    task_id: str
+    turn_id: str
     last_message_id: str  # Redis Stream message ID for resumption
-    operation_id: str  # Operation ID for completion tracking
-    tool_name: str  # Name of the tool being executed
 
 
 class SessionDetailResponse(BaseModel):
@@ -132,22 +123,13 @@ class ListSessionsResponse(BaseModel):
     total: int
 
 
-class CancelTaskResponse(BaseModel):
-    """Response model for the cancel task endpoint."""
+class CancelSessionResponse(BaseModel):
+    """Response model for the cancel session endpoint."""
 
     cancelled: bool
-    task_id: str | None = None
     reason: str | None = None
 
 
-class OperationCompleteRequest(BaseModel):
-    """Request model for external completion webhook."""
-
-    success: bool
-    result: dict | str | None = None
-    error: str | None = None
-
-
 # ========== Routes ==========
 
 
@@ -270,7 +252,7 @@ async def get_session(
     Retrieve the details of a specific chat session.
 
     Looks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.
-    If there's an active stream for this session, returns the task_id for reconnection.
+    If there's an active stream for this session, returns active_stream info for reconnection.
 
     Args:
         session_id: The unique identifier for the desired chat session.
@@ -288,28 +270,21 @@ async def get_session(
 
     # Check if there's an active stream for this session
     active_stream_info = None
-    active_task, last_message_id = await stream_registry.get_active_task_for_session(
+    active_session, last_message_id = await stream_registry.get_active_session(
         session_id, user_id
     )
     logger.info(
-        f"[GET_SESSION] session={session_id}, active_task={active_task is not None}, "
+        f"[GET_SESSION] session={session_id}, active_session={active_session is not None}, "
         f"msg_count={len(messages)}, last_role={messages[-1].get('role') if messages else 'none'}"
     )
-    if active_task:
-        # Filter out the in-progress assistant message from the session response.
-        # The client will receive the complete assistant response through the SSE
-        # stream replay instead, preventing duplicate content.
-        if messages and messages[-1].get("role") == "assistant":
-            messages = messages[:-1]
-
-        # Use "0-0" as last_message_id to replay the stream from the beginning.
-        # Since we filtered out the cached assistant message, the client needs
-        # the full stream to reconstruct the response.
+    if active_session:
+        # Keep the assistant message (including tool_calls) so the frontend can
+        # render the correct tool UI (e.g. CreateAgent with mini game).
+        # convertChatSessionToUiMessages handles isComplete=false by setting
+        # tool parts without output to state "input-available".
         active_stream_info = ActiveStreamInfo(
-            task_id=active_task.task_id,
-            last_message_id="0-0",
-            operation_id=active_task.operation_id,
-            tool_name=active_task.tool_name,
+            turn_id=active_session.turn_id,
+            last_message_id=last_message_id,
         )
 
     return SessionDetailResponse(
@@ -329,7 +304,7 @@ async def get_session(
 async def cancel_session_task(
     session_id: str,
     user_id: Annotated[str | None, Depends(auth.get_user_id)],
-) -> CancelTaskResponse:
+) -> CancelSessionResponse:
     """Cancel the active streaming task for a session.
 
     Publishes a cancel event to the executor via RabbitMQ FANOUT, then
@@ -338,39 +313,33 @@ async def cancel_session_task(
     """
     await _validate_and_get_session(session_id, user_id)
 
-    active_task, _ = await stream_registry.get_active_task_for_session(
-        session_id, user_id
-    )
-    if not active_task:
-        return CancelTaskResponse(cancelled=False, reason="no_active_task")
+    active_session, _ = await stream_registry.get_active_session(session_id, user_id)
+    if not active_session:
+        return CancelSessionResponse(cancelled=True, reason="no_active_session")
 
-    task_id = active_task.task_id
-    await enqueue_cancel_task(task_id)
-    logger.info(
-        f"[CANCEL] Published cancel for task ...{task_id[-8:]} "
-        f"session ...{session_id[-8:]}"
-    )
+    await enqueue_cancel_task(session_id)
+    logger.info(f"[CANCEL] Published cancel for session ...{session_id[-8:]}")
 
     # Poll until the executor confirms the task is no longer running.
-    # Keep max_wait below typical reverse-proxy read timeouts.
     poll_interval = 0.5
     max_wait = 5.0
     waited = 0.0
     while waited < max_wait:
         await asyncio.sleep(poll_interval)
         waited += poll_interval
-        task = await stream_registry.get_task(task_id)
-        if task is None or task.status != "running":
+        session_state = await stream_registry.get_session(session_id)
+        if session_state is None or session_state.status != "running":
             logger.info(
-                f"[CANCEL] Task ...{task_id[-8:]} confirmed stopped "
-                f"(status={task.status if task else 'gone'}) after {waited:.1f}s"
+                f"[CANCEL] Session ...{session_id[-8:]} confirmed stopped "
+                f"(status={session_state.status if session_state else 'gone'}) after {waited:.1f}s"
             )
-            return CancelTaskResponse(cancelled=True, task_id=task_id)
+            return CancelSessionResponse(cancelled=True)
 
-    logger.warning(f"[CANCEL] Task ...{task_id[-8:]} not confirmed after {max_wait}s")
-    return CancelTaskResponse(
-        cancelled=True, task_id=task_id, reason="cancel_published_not_confirmed"
+    logger.warning(
+        f"[CANCEL] Session ...{session_id[-8:]} not confirmed after {max_wait}s, force-completing"
     )
+    await stream_registry.mark_session_completed(session_id, error_message="Cancelled")
+    return CancelSessionResponse(cancelled=True)
 
 
 @router.post(
@@ -390,16 +359,15 @@ async def stream_chat_post(
       - Tool execution results
 
     The AI generation runs in a background task that continues even if the client disconnects.
-    All chunks are written to Redis for reconnection support. If the client disconnects,
-    they can reconnect using GET /tasks/{task_id}/stream to resume from where they left off.
+    All chunks are written to a per-turn Redis stream for reconnection support. If the client
+    disconnects, they can reconnect using GET /sessions/{session_id}/stream to resume.
 
     Args:
         session_id: The chat session identifier to associate with the streamed messages.
         request: Request body containing message, is_user_message, and optional context.
         user_id: Optional authenticated user ID.
     Returns:
-        StreamingResponse: SSE-formatted response chunks. First chunk is a "start" event
-        containing the task_id for reconnection.
+        StreamingResponse: SSE-formatted response chunks.
 
     """
     import asyncio
@@ -446,35 +414,35 @@ async def stream_chat_post(
         logger.info(f"[STREAM] User message saved for session {session_id}")
 
     # Create a task in the stream registry for reconnection support
-    task_id = str(uuid_module.uuid4())
-    operation_id = str(uuid_module.uuid4())
-    log_meta["task_id"] = task_id
+    turn_id = str(uuid4())
+    log_meta["turn_id"] = turn_id
 
-    task_create_start = time.perf_counter()
-    await stream_registry.create_task(
-        task_id=task_id,
+    session_create_start = time.perf_counter()
+    await stream_registry.create_session(
         session_id=session_id,
         user_id=user_id,
-        tool_call_id="chat_stream",  # Not a tool call, but needed for the model
+        tool_call_id="chat_stream",
         tool_name="chat",
-        operation_id=operation_id,
+        turn_id=turn_id,
     )
     logger.info(
-        f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start) * 1000:.1f}ms",
+        f"[TIMING] create_session completed in {(time.perf_counter() - session_create_start) * 1000:.1f}ms",
         extra={
             "json_fields": {
                 **log_meta,
-                "duration_ms": (time.perf_counter() - task_create_start) * 1000,
+                "duration_ms": (time.perf_counter() - session_create_start) * 1000,
             }
         },
     )
 
-    await enqueue_copilot_task(
-        task_id=task_id,
+    # Per-turn stream is always fresh (unique turn_id), subscribe from beginning
+    subscribe_from_id = "0-0"
+
+    await enqueue_copilot_turn(
         session_id=session_id,
         user_id=user_id,
-        operation_id=operation_id,
         message=request.message,
+        turn_id=turn_id,
         is_user_message=request.is_user_message,
         context=request.context,
     )
@@ -491,7 +459,7 @@ async def stream_chat_post(
 
         event_gen_start = time_module.perf_counter()
         logger.info(
-            f"[TIMING] event_generator STARTED, task={task_id}, session={session_id}, "
+            f"[TIMING] event_generator STARTED, turn={turn_id}, session={session_id}, "
             f"user={user_id}",
             extra={"json_fields": log_meta},
         )
@@ -499,11 +467,12 @@ async def stream_chat_post(
         first_chunk_yielded = False
         chunks_yielded = 0
         try:
-            # Subscribe to the task stream (this replays existing messages + live updates)
-            subscriber_queue = await stream_registry.subscribe_to_task(
-                task_id=task_id,
+            # Subscribe from the position we captured before enqueuing
+            # This avoids replaying old messages while catching all new ones
+            subscriber_queue = await stream_registry.subscribe_to_session(
+                session_id=session_id,
                 user_id=user_id,
-                last_message_id="0-0",  # Get all messages from the beginning
+                last_message_id=subscribe_from_id,
             )
 
             if subscriber_queue is None:
@@ -586,19 +555,19 @@ async def stream_chat_post(
             # Unsubscribe when client disconnects or stream ends
             if subscriber_queue is not None:
                 try:
-                    await stream_registry.unsubscribe_from_task(
-                        task_id, subscriber_queue
+                    await stream_registry.unsubscribe_from_session(
+                        session_id, subscriber_queue
                     )
                 except Exception as unsub_err:
                     logger.error(
-                        f"Error unsubscribing from task {task_id}: {unsub_err}",
+                        f"Error unsubscribing from session {session_id}: {unsub_err}",
                         exc_info=True,
                     )
             # AI SDK protocol termination - always yield even if unsubscribe fails
             total_time = time_module.perf_counter() - event_gen_start
             logger.info(
                 f"[TIMING] event_generator FINISHED in {total_time:.2f}s; "
-                f"task={task_id}, session={session_id}, n_chunks={chunks_yielded}",
+                f"turn={turn_id}, session={session_id}, n_chunks={chunks_yielded}",
                 extra={
                     "json_fields": {
                         **log_meta,
@@ -645,17 +614,22 @@ async def resume_session_stream(
     """
     import asyncio
 
-    active_task, _last_id = await stream_registry.get_active_task_for_session(
+    active_session, last_message_id = await stream_registry.get_active_session(
         session_id, user_id
     )
 
-    if not active_task:
+    if not active_session:
         return Response(status_code=204)
 
-    subscriber_queue = await stream_registry.subscribe_to_task(
-        task_id=active_task.task_id,
+    # Subscribe from the beginning ("0-0") to replay all chunks for this turn.
+    # This is necessary because hydrated messages filter out incomplete tool calls
+    # to avoid "No tool invocation found" errors. The resume stream delivers
+    # those tool calls fresh with proper SDK state.
+    # The AI SDK's deduplication will handle any duplicate chunks.
+    subscriber_queue = await stream_registry.subscribe_to_session(
+        session_id=session_id,
         user_id=user_id,
-        last_message_id="0-0",  # Full replay so useChat rebuilds the message
+        last_message_id="0-0",
     )
 
     if subscriber_queue is None:
@@ -691,12 +665,12 @@ async def resume_session_stream(
             logger.error(f"Error in resume stream for session {session_id}: {e}")
         finally:
             try:
-                await stream_registry.unsubscribe_from_task(
-                    active_task.task_id, subscriber_queue
+                await stream_registry.unsubscribe_from_session(
+                    session_id, subscriber_queue
                 )
             except Exception as unsub_err:
                 logger.error(
-                    f"Error unsubscribing from task {active_task.task_id}: {unsub_err}",
+                    f"Error unsubscribing from session {active_session.session_id}: {unsub_err}",
                     exc_info=True,
                 )
             logger.info(
@@ -747,229 +721,6 @@ async def session_assign_user(
     return {"status": "ok"}
 
 
-# ========== Task Streaming (SSE Reconnection) ==========
-
-
-@router.get(
-    "/tasks/{task_id}/stream",
-)
-async def stream_task(
-    task_id: str,
-    user_id: str | None = Depends(auth.get_user_id),
-    last_message_id: str = Query(
-        default="0-0",
-        description="Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay.",
-    ),
-):
-    """
-    Reconnect to a long-running task's SSE stream.
-
-    When a long-running operation (like agent generation) starts, the client
-    receives a task_id. If the connection drops, the client can reconnect
-    using this endpoint to resume receiving updates.
-
-    Args:
-        task_id: The task ID from the operation_started response.
-        user_id: Authenticated user ID for ownership validation.
-        last_message_id: Last Redis Stream message ID received ("0-0" for full replay).
-
-    Returns:
-        StreamingResponse: SSE-formatted response chunks starting after last_message_id.
-
-    Raises:
-        HTTPException: 404 if task not found, 410 if task expired, 403 if access denied.
-    """
-    # Check task existence and expiry before subscribing
-    task, error_code = await stream_registry.get_task_with_expiry_info(task_id)
-
-    if error_code == "TASK_EXPIRED":
-        raise HTTPException(
-            status_code=410,
-            detail={
-                "code": "TASK_EXPIRED",
-                "message": "This operation has expired. Please try again.",
-            },
-        )
-
-    if error_code == "TASK_NOT_FOUND":
-        raise HTTPException(
-            status_code=404,
-            detail={
-                "code": "TASK_NOT_FOUND",
-                "message": f"Task {task_id} not found.",
-            },
-        )
-
-    # Validate ownership if task has an owner
-    if task and task.user_id and user_id != task.user_id:
-        raise HTTPException(
-            status_code=403,
-            detail={
-                "code": "ACCESS_DENIED",
-                "message": "You do not have access to this task.",
-            },
-        )
-
-    # Get subscriber queue from stream registry
-    subscriber_queue = await stream_registry.subscribe_to_task(
-        task_id=task_id,
-        user_id=user_id,
-        last_message_id=last_message_id,
-    )
-
-    if subscriber_queue is None:
-        raise HTTPException(
-            status_code=404,
-            detail={
-                "code": "TASK_NOT_FOUND",
-                "message": f"Task {task_id} not found or access denied.",
-            },
-        )
-
-    async def event_generator() -> AsyncGenerator[str, None]:
-        heartbeat_interval = 15.0  # Send heartbeat every 15 seconds
-        try:
-            while True:
-                try:
-                    # Wait for next chunk with timeout for heartbeats
-                    chunk = await asyncio.wait_for(
-                        subscriber_queue.get(), timeout=heartbeat_interval
-                    )
-                    yield chunk.to_sse()
-
-                    # Check for finish signal
-                    if isinstance(chunk, StreamFinish):
-                        break
-                except asyncio.TimeoutError:
-                    # Send heartbeat to keep connection alive
-                    yield StreamHeartbeat().to_sse()
-        except Exception as e:
-            logger.error(f"Error in task stream {task_id}: {e}", exc_info=True)
-        finally:
-            # Unsubscribe when client disconnects or stream ends
-            try:
-                await stream_registry.unsubscribe_from_task(task_id, subscriber_queue)
-            except Exception as unsub_err:
-                logger.error(
-                    f"Error unsubscribing from task {task_id}: {unsub_err}",
-                    exc_info=True,
-                )
-            # AI SDK protocol termination - always yield even if unsubscribe fails
-            yield "data: [DONE]\n\n"
-
-    return StreamingResponse(
-        event_generator(),
-        media_type="text/event-stream",
-        headers={
-            "Cache-Control": "no-cache",
-            "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",
-            "x-vercel-ai-ui-message-stream": "v1",
-        },
-    )
-
-
-@router.get(
-    "/tasks/{task_id}",
-)
-async def get_task_status(
-    task_id: str,
-    user_id: str | None = Depends(auth.get_user_id),
-) -> dict:
-    """
-    Get the status of a long-running task.
-
-    Args:
-        task_id: The task ID to check.
-        user_id: Authenticated user ID for ownership validation.
-
-    Returns:
-        dict: Task status including task_id, status, tool_name, and operation_id.
-
-    Raises:
-        NotFoundError: If task_id is not found or user doesn't have access.
-    """
-    task = await stream_registry.get_task(task_id)
-
-    if task is None:
-        raise NotFoundError(f"Task {task_id} not found.")
-
-    # Validate ownership - if task has an owner, requester must match
-    if task.user_id and user_id != task.user_id:
-        raise NotFoundError(f"Task {task_id} not found.")
-
-    return {
-        "task_id": task.task_id,
-        "session_id": task.session_id,
-        "status": task.status,
-        "tool_name": task.tool_name,
-        "operation_id": task.operation_id,
-        "created_at": task.created_at.isoformat(),
-    }
-
-
-# ========== External Completion Webhook ==========
-
-
-@router.post(
-    "/operations/{operation_id}/complete",
-    status_code=200,
-)
-async def complete_operation(
-    operation_id: str,
-    request: OperationCompleteRequest,
-    x_api_key: str | None = Header(default=None),
-) -> dict:
-    """
-    External completion webhook for long-running operations.
-
-    Called by Agent Generator (or other services) when an operation completes.
-    This triggers the stream registry to publish completion and continue LLM generation.
-
-    Args:
-        operation_id: The operation ID to complete.
-        request: Completion payload with success status and result/error.
-        x_api_key: Internal API key for authentication.
-
-    Returns:
-        dict: Status of the completion.
-
-    Raises:
-        HTTPException: If API key is invalid or operation not found.
-    """
-    # Validate internal API key - reject if not configured or invalid
-    if not config.internal_api_key:
-        logger.error(
-            "Operation complete webhook rejected: CHAT_INTERNAL_API_KEY not configured"
-        )
-        raise HTTPException(
-            status_code=503,
-            detail="Webhook not available: internal API key not configured",
-        )
-    if x_api_key != config.internal_api_key:
-        raise HTTPException(status_code=401, detail="Invalid API key")
-
-    # Find task by operation_id
-    task = await stream_registry.find_task_by_operation_id(operation_id)
-    if task is None:
-        raise HTTPException(
-            status_code=404,
-            detail=f"Operation {operation_id} not found",
-        )
-
-    logger.info(
-        f"Received completion webhook for operation {operation_id} "
-        f"(task_id={task.task_id}, success={request.success})"
-    )
-
-    if request.success:
-        await process_operation_success(task, request.result)
-    else:
-        await process_operation_failure(task, request.error)
-
-    return {"status": "ok", "task_id": task.task_id}
-
-
 # ========== Configuration ==========
 
 
@@ -1050,9 +801,6 @@ ToolResponseUnion = (
     | BlockOutputResponse
     | DocSearchResultsResponse
     | DocPageResponse
-    | OperationStartedResponse
-    | OperationPendingResponse
-    | OperationInProgressResponse
 )
 
 

autogpt_platform/backend/backend/api/features/v1.py

@@ -126,6 +126,9 @@ v1_router = APIRouter()
 ########################################################
 
 
+_tally_background_tasks: set[asyncio.Task] = set()
+
+
 @v1_router.post(
     "/auth/user",
     summary="Get or create user",
@@ -134,6 +137,24 @@ v1_router = APIRouter()
 )
 async def get_or_create_user_route(user_data: dict = Security(get_jwt_payload)):
     user = await get_or_create_user(user_data)
+
+    # Fire-and-forget: populate business understanding from Tally form.
+    # We use created_at proximity instead of an is_new flag because
+    # get_or_create_user is cached — a separate is_new return value would be
+    # unreliable on repeated calls within the cache TTL.
+    age_seconds = (datetime.now(timezone.utc) - user.created_at).total_seconds()
+    if age_seconds < 30:
+        try:
+            from backend.data.tally import populate_understanding_from_tally
+
+            task = asyncio.create_task(
+                populate_understanding_from_tally(user.id, user.email)
+            )
+            _tally_background_tasks.add(task)
+            task.add_done_callback(_tally_background_tasks.discard)
+        except Exception:
+            logger.debug("Failed to start Tally population task", exc_info=True)
+
     return user.model_dump()
 
 

autogpt_platform/backend/backend/api/features/v1_test.py

@@ -1,5 +1,5 @@
 import json
-from datetime import datetime
+from datetime import datetime, timezone
 from io import BytesIO
 from unittest.mock import AsyncMock, Mock, patch
 
@@ -43,6 +43,7 @@ def test_get_or_create_user_route(
 ) -> None:
     """Test get or create user endpoint"""
     mock_user = Mock()
+    mock_user.created_at = datetime.now(timezone.utc)
     mock_user.model_dump.return_value = {
         "id": test_user_id,
         "email": "test@example.com",

autogpt_platform/backend/backend/api/rest_api.py

@@ -42,10 +42,6 @@ import backend.integrations.webhooks.utils
 import backend.util.service
 import backend.util.settings
 from backend.blocks.llm import DEFAULT_LLM_MODEL
-from backend.copilot.completion_consumer import (
-    start_completion_consumer,
-    stop_completion_consumer,
-)
 from backend.data.model import Credentials
 from backend.integrations.providers import ProviderName
 from backend.monitoring.instrumentation import instrument_fastapi
@@ -123,21 +119,9 @@ async def lifespan_context(app: fastapi.FastAPI):
     await backend.data.graph.migrate_llm_models(DEFAULT_LLM_MODEL)
     await backend.integrations.webhooks.utils.migrate_legacy_triggered_graphs()
 
-    # Start chat completion consumer for Redis Streams notifications
-    try:
-        await start_completion_consumer()
-    except Exception as e:
-        logger.warning(f"Could not start chat completion consumer: {e}")
-
     with launch_darkly_context():
         yield
 
-    # Stop chat completion consumer
-    try:
-        await stop_completion_consumer()
-    except Exception as e:
-        logger.warning(f"Error stopping chat completion consumer: {e}")
-
     try:
         await shutdown_cloud_storage_handler()
     except Exception as e:

autogpt_platform/backend/backend/app.py

@@ -24,7 +24,7 @@ def run_processes(*processes: "AppProcess", **kwargs):
         # Run the last process in the foreground.
         processes[-1].start(background=False, **kwargs)
     finally:
-        for process in processes:
+        for process in reversed(processes):
             try:
                 process.stop()
             except Exception as e:

autogpt_platform/backend/backend/copilot/completion_consumer.py

@@ -1,349 +0,0 @@
-"""Redis Streams consumer for operation completion messages.
-
-This module provides a consumer (ChatCompletionConsumer) that listens for
-completion notifications (OperationCompleteMessage) from external services
-(like Agent Generator) and triggers the appropriate stream registry and
-chat service updates via process_operation_success/process_operation_failure.
-
-Why Redis Streams instead of RabbitMQ?
---------------------------------------
-While the project typically uses RabbitMQ for async task queues (e.g., execution
-queue), Redis Streams was chosen for chat completion notifications because:
-
-1. **Unified Infrastructure**: The SSE reconnection feature already uses Redis
-   Streams (via stream_registry) for message persistence and replay. Using Redis
-   Streams for completion notifications keeps all chat streaming infrastructure
-   in one system, simplifying operations and reducing cross-system coordination.
-
-2. **Message Replay**: Redis Streams support XREAD with arbitrary message IDs,
-   allowing consumers to replay missed messages after reconnection. This aligns
-   with the SSE reconnection pattern where clients can resume from last_message_id.
-
-3. **Consumer Groups with XAUTOCLAIM**: Redis consumer groups provide automatic
-   load balancing across pods with explicit message claiming (XAUTOCLAIM) for
-   recovering from dead consumers - ideal for the completion callback pattern.
-
-4. **Lower Latency**: For real-time SSE updates, Redis (already in-memory for
-   stream_registry) provides lower latency than an additional RabbitMQ hop.
-
-5. **Atomicity with Task State**: Completion processing often needs to update
-   task metadata stored in Redis. Keeping both in Redis enables simpler
-   transactional semantics without distributed coordination.
-
-The consumer uses Redis Streams with consumer groups for reliable message
-processing across multiple platform pods, with XAUTOCLAIM for reclaiming
-stale pending messages from dead consumers.
-"""
-
-import asyncio
-import logging
-import uuid
-from typing import Any
-
-import orjson
-from pydantic import BaseModel
-from redis.exceptions import ResponseError
-
-from backend.data.redis_client import get_redis_async
-
-from . import stream_registry
-from .completion_handler import process_operation_failure, process_operation_success
-from .config import ChatConfig
-
-logger = logging.getLogger(__name__)
-config = ChatConfig()
-
-
-class OperationCompleteMessage(BaseModel):
-    """Message format for operation completion notifications."""
-
-    operation_id: str
-    task_id: str
-    success: bool
-    result: dict | str | None = None
-    error: str | None = None
-
-
-class ChatCompletionConsumer:
-    """Consumer for chat operation completion messages from Redis Streams.
-
-    Database operations are handled through the chat_db() accessor, which
-    routes through DatabaseManager RPC when Prisma is not directly connected.
-
-    Uses Redis consumer groups to allow multiple platform pods to consume
-    messages reliably with automatic redelivery on failure.
-    """
-
-    def __init__(self):
-        self._consumer_task: asyncio.Task | None = None
-        self._running = False
-        self._consumer_name = f"consumer-{uuid.uuid4().hex[:8]}"
-
-    async def start(self) -> None:
-        """Start the completion consumer."""
-        if self._running:
-            logger.warning("Completion consumer already running")
-            return
-
-        # Create consumer group if it doesn't exist
-        try:
-            redis = await get_redis_async()
-            await redis.xgroup_create(
-                config.stream_completion_name,
-                config.stream_consumer_group,
-                id="0",
-                mkstream=True,
-            )
-            logger.info(
-                f"Created consumer group '{config.stream_consumer_group}' "
-                f"on stream '{config.stream_completion_name}'"
-            )
-        except ResponseError as e:
-            if "BUSYGROUP" in str(e):
-                logger.debug(
-                    f"Consumer group '{config.stream_consumer_group}' already exists"
-                )
-            else:
-                raise
-
-        self._running = True
-        self._consumer_task = asyncio.create_task(self._consume_messages())
-        logger.info(
-            f"Chat completion consumer started (consumer: {self._consumer_name})"
-        )
-
-    async def stop(self) -> None:
-        """Stop the completion consumer."""
-        self._running = False
-
-        if self._consumer_task:
-            self._consumer_task.cancel()
-            try:
-                await self._consumer_task
-            except asyncio.CancelledError:
-                pass
-            self._consumer_task = None
-
-        logger.info("Chat completion consumer stopped")
-
-    async def _consume_messages(self) -> None:
-        """Main message consumption loop with retry logic."""
-        max_retries = 10
-        retry_delay = 5  # seconds
-        retry_count = 0
-        block_timeout = 5000  # milliseconds
-
-        while self._running and retry_count < max_retries:
-            try:
-                redis = await get_redis_async()
-
-                # Reset retry count on successful connection
-                retry_count = 0
-
-                while self._running:
-                    # First, claim any stale pending messages from dead consumers
-                    # Redis does NOT auto-redeliver pending messages; we must explicitly
-                    # claim them using XAUTOCLAIM
-                    try:
-                        claimed_result = await redis.xautoclaim(
-                            name=config.stream_completion_name,
-                            groupname=config.stream_consumer_group,
-                            consumername=self._consumer_name,
-                            min_idle_time=config.stream_claim_min_idle_ms,
-                            start_id="0-0",
-                            count=10,
-                        )
-                        # xautoclaim returns: (next_start_id, [(id, data), ...], [deleted_ids])
-                        if claimed_result and len(claimed_result) >= 2:
-                            claimed_entries = claimed_result[1]
-                            if claimed_entries:
-                                logger.info(
-                                    f"Claimed {len(claimed_entries)} stale pending messages"
-                                )
-                                for entry_id, data in claimed_entries:
-                                    if not self._running:
-                                        return
-                                    await self._process_entry(redis, entry_id, data)
-                    except Exception as e:
-                        logger.warning(f"XAUTOCLAIM failed (non-fatal): {e}")
-
-                    # Read new messages from the stream
-                    messages = await redis.xreadgroup(
-                        groupname=config.stream_consumer_group,
-                        consumername=self._consumer_name,
-                        streams={config.stream_completion_name: ">"},
-                        block=block_timeout,
-                        count=10,
-                    )
-
-                    if not messages:
-                        continue
-
-                    for stream_name, entries in messages:
-                        for entry_id, data in entries:
-                            if not self._running:
-                                return
-                            await self._process_entry(redis, entry_id, data)
-
-            except asyncio.CancelledError:
-                logger.info("Consumer cancelled")
-                return
-            except Exception as e:
-                retry_count += 1
-                logger.error(
-                    f"Consumer error (retry {retry_count}/{max_retries}): {e}",
-                    exc_info=True,
-                )
-                if self._running and retry_count < max_retries:
-                    await asyncio.sleep(retry_delay)
-                else:
-                    logger.error("Max retries reached, stopping consumer")
-                    return
-
-    async def _process_entry(
-        self, redis: Any, entry_id: str, data: dict[str, Any]
-    ) -> None:
-        """Process a single stream entry and acknowledge it on success.
-
-        Args:
-            redis: Redis client connection
-            entry_id: The stream entry ID
-            data: The entry data dict
-        """
-        try:
-            # Handle the message
-            message_data = data.get("data")
-            if message_data:
-                await self._handle_message(
-                    message_data.encode()
-                    if isinstance(message_data, str)
-                    else message_data
-                )
-
-            # Acknowledge the message after successful processing
-            await redis.xack(
-                config.stream_completion_name,
-                config.stream_consumer_group,
-                entry_id,
-            )
-        except Exception as e:
-            logger.error(
-                f"Error processing completion message {entry_id}: {e}",
-                exc_info=True,
-            )
-            # Message remains in pending state and will be claimed by
-            # XAUTOCLAIM after min_idle_time expires
-
-    async def _handle_message(self, body: bytes) -> None:
-        """Handle a completion message."""
-        try:
-            data = orjson.loads(body)
-            message = OperationCompleteMessage(**data)
-        except Exception as e:
-            logger.error(f"Failed to parse completion message: {e}")
-            return
-
-        logger.info(
-            f"[COMPLETION] Received completion for operation {message.operation_id} "
-            f"(task_id={message.task_id}, success={message.success})"
-        )
-
-        # Find task in registry
-        task = await stream_registry.find_task_by_operation_id(message.operation_id)
-        if task is None:
-            task = await stream_registry.get_task(message.task_id)
-
-        if task is None:
-            logger.warning(
-                f"[COMPLETION] Task not found for operation {message.operation_id} "
-                f"(task_id={message.task_id})"
-            )
-            return
-
-        logger.info(
-            f"[COMPLETION] Found task: task_id={task.task_id}, "
-            f"session_id={task.session_id}, tool_call_id={task.tool_call_id}"
-        )
-
-        # Guard against empty task fields
-        if not task.task_id or not task.session_id or not task.tool_call_id:
-            logger.error(
-                f"[COMPLETION] Task has empty critical fields! "
-                f"task_id={task.task_id!r}, session_id={task.session_id!r}, "
-                f"tool_call_id={task.tool_call_id!r}"
-            )
-            return
-
-        if message.success:
-            await self._handle_success(task, message)
-        else:
-            await self._handle_failure(task, message)
-
-    async def _handle_success(
-        self,
-        task: stream_registry.ActiveTask,
-        message: OperationCompleteMessage,
-    ) -> None:
-        """Handle successful operation completion."""
-        await process_operation_success(task, message.result)
-
-    async def _handle_failure(
-        self,
-        task: stream_registry.ActiveTask,
-        message: OperationCompleteMessage,
-    ) -> None:
-        """Handle failed operation completion."""
-        await process_operation_failure(task, message.error)
-
-
-# Module-level consumer instance
-_consumer: ChatCompletionConsumer | None = None
-
-
-async def start_completion_consumer() -> None:
-    """Start the global completion consumer."""
-    global _consumer
-    if _consumer is None:
-        _consumer = ChatCompletionConsumer()
-    await _consumer.start()
-
-
-async def stop_completion_consumer() -> None:
-    """Stop the global completion consumer."""
-    global _consumer
-    if _consumer:
-        await _consumer.stop()
-        _consumer = None
-
-
-async def publish_operation_complete(
-    operation_id: str,
-    task_id: str,
-    success: bool,
-    result: dict | str | None = None,
-    error: str | None = None,
-) -> None:
-    """Publish an operation completion message to Redis Streams.
-
-    Args:
-        operation_id: The operation ID that completed.
-        task_id: The task ID associated with the operation.
-        success: Whether the operation succeeded.
-        result: The result data (for success).
-        error: The error message (for failure).
-    """
-    message = OperationCompleteMessage(
-        operation_id=operation_id,
-        task_id=task_id,
-        success=success,
-        result=result,
-        error=error,
-    )
-
-    redis = await get_redis_async()
-    await redis.xadd(
-        config.stream_completion_name,
-        {"data": message.model_dump_json()},
-        maxlen=config.stream_max_length,
-    )
-    logger.info(f"Published completion for operation {operation_id}")

autogpt_platform/backend/backend/copilot/completion_handler.py

@@ -1,329 +0,0 @@
-"""Shared completion handling for operation success and failure.
-
-This module provides common logic for handling operation completion from both:
-- The Redis Streams consumer (completion_consumer.py)
-- The HTTP webhook endpoint (routes.py)
-"""
-
-import logging
-from typing import Any
-
-import orjson
-
-from backend.data.db_accessors import chat_db
-
-from . import service as chat_service
-from . import stream_registry
-from .response_model import StreamError, StreamToolOutputAvailable
-from .tools.models import ErrorResponse
-
-logger = logging.getLogger(__name__)
-
-# Tools that produce agent_json that needs to be saved to library
-AGENT_GENERATION_TOOLS = {"create_agent", "edit_agent"}
-
-# Keys that should be stripped from agent_json when returning in error responses
-SENSITIVE_KEYS = frozenset(
-    {
-        "api_key",
-        "apikey",
-        "api_secret",
-        "password",
-        "secret",
-        "credentials",
-        "credential",
-        "token",
-        "access_token",
-        "refresh_token",
-        "private_key",
-        "privatekey",
-        "auth",
-        "authorization",
-    }
-)
-
-
-def _sanitize_agent_json(obj: Any) -> Any:
-    """Recursively sanitize agent_json by removing sensitive keys.
-
-    Args:
-        obj: The object to sanitize (dict, list, or primitive)
-
-    Returns:
-        Sanitized copy with sensitive keys removed/redacted
-    """
-    if isinstance(obj, dict):
-        return {
-            k: "[REDACTED]" if k.lower() in SENSITIVE_KEYS else _sanitize_agent_json(v)
-            for k, v in obj.items()
-        }
-    elif isinstance(obj, list):
-        return [_sanitize_agent_json(item) for item in obj]
-    else:
-        return obj
-
-
-class ToolMessageUpdateError(Exception):
-    """Raised when updating a tool message in the database fails."""
-
-    pass
-
-
-async def _update_tool_message(
-    session_id: str,
-    tool_call_id: str,
-    content: str,
-) -> None:
-    """Update tool message in database using the chat_db accessor.
-
-    Routes through DatabaseManager RPC when Prisma is not directly
-    connected (e.g. in the CoPilot Executor microservice).
-
-    Args:
-        session_id: The session ID
-        tool_call_id: The tool call ID to update
-        content: The new content for the message
-
-    Raises:
-        ToolMessageUpdateError: If the database update fails.
-    """
-    try:
-        updated = await chat_db().update_tool_message_content(
-            session_id=session_id,
-            tool_call_id=tool_call_id,
-            new_content=content,
-        )
-        if not updated:
-            raise ToolMessageUpdateError(
-                f"No message found with tool_call_id="
-                f"{tool_call_id} in session {session_id}"
-            )
-    except ToolMessageUpdateError:
-        raise
-    except Exception as e:
-        logger.error(
-            f"[COMPLETION] Failed to update tool message: {e}",
-            exc_info=True,
-        )
-        raise ToolMessageUpdateError(
-            f"Failed to update tool message for tool call #{tool_call_id}: {e}"
-        ) from e
-
-
-def serialize_result(result: dict | list | str | int | float | bool | None) -> str:
-    """Serialize result to JSON string with sensible defaults.
-
-    Args:
-        result: The result to serialize. Can be a dict, list, string,
-            number, boolean, or None.
-
-    Returns:
-        JSON string representation of the result. Returns '{"status": "completed"}'
-        only when result is explicitly None.
-    """
-    if isinstance(result, str):
-        return result
-    if result is None:
-        return '{"status": "completed"}'
-    return orjson.dumps(result).decode("utf-8")
-
-
-async def _save_agent_from_result(
-    result: dict[str, Any],
-    user_id: str | None,
-    tool_name: str,
-) -> dict[str, Any]:
-    """Save agent to library if result contains agent_json.
-
-    Args:
-        result: The result dict that may contain agent_json
-        user_id: The user ID to save the agent for
-        tool_name: The tool name (create_agent or edit_agent)
-
-    Returns:
-        Updated result dict with saved agent details, or original result if no agent_json
-    """
-    if not user_id:
-        logger.warning("[COMPLETION] Cannot save agent: no user_id in task")
-        return result
-
-    agent_json = result.get("agent_json")
-    if not agent_json:
-        logger.warning(
-            f"[COMPLETION] {tool_name} completed but no agent_json in result"
-        )
-        return result
-
-    try:
-        from .tools.agent_generator import save_agent_to_library
-
-        is_update = tool_name == "edit_agent"
-        created_graph, library_agent = await save_agent_to_library(
-            agent_json, user_id, is_update=is_update
-        )
-
-        logger.info(
-            f"[COMPLETION] Saved agent '{created_graph.name}' to library "
-            f"(graph_id={created_graph.id}, library_agent_id={library_agent.id})"
-        )
-
-        # Return a response similar to AgentSavedResponse
-        return {
-            "type": "agent_saved",
-            "message": f"Agent '{created_graph.name}' has been saved to your library!",
-            "agent_id": created_graph.id,
-            "agent_name": created_graph.name,
-            "library_agent_id": library_agent.id,
-            "library_agent_link": f"/library/agents/{library_agent.id}",
-            "agent_page_link": f"/build?flowID={created_graph.id}",
-        }
-    except Exception as e:
-        logger.error(
-            f"[COMPLETION] Failed to save agent to library: {e}",
-            exc_info=True,
-        )
-        # Return error but don't fail the whole operation
-        # Sanitize agent_json to remove sensitive keys before returning
-        return {
-            "type": "error",
-            "message": f"Agent was generated but failed to save: {str(e)}",
-            "error": str(e),
-            "agent_json": _sanitize_agent_json(agent_json),
-        }
-
-
-async def process_operation_success(
-    task: stream_registry.ActiveTask,
-    result: dict | str | None,
-) -> None:
-    """Handle successful operation completion.
-
-    Publishes the result to the stream registry, updates the database,
-    generates LLM continuation, and marks the task as completed.
-
-    Args:
-        task: The active task that completed
-        result: The result data from the operation
-
-    Raises:
-        ToolMessageUpdateError: If the database update fails. The task
-            will be marked as failed instead of completed.
-    """
-    # For agent generation tools, save the agent to library
-    if task.tool_name in AGENT_GENERATION_TOOLS and isinstance(result, dict):
-        result = await _save_agent_from_result(result, task.user_id, task.tool_name)
-
-    # Serialize result for output (only substitute default when result is exactly None)
-    result_output = result if result is not None else {"status": "completed"}
-    output_str = (
-        result_output
-        if isinstance(result_output, str)
-        else orjson.dumps(result_output).decode("utf-8")
-    )
-
-    # Publish result to stream registry
-    await stream_registry.publish_chunk(
-        task.task_id,
-        StreamToolOutputAvailable(
-            toolCallId=task.tool_call_id,
-            toolName=task.tool_name,
-            output=output_str,
-            success=True,
-        ),
-    )
-
-    # Update pending operation in database
-    # If this fails, we must not continue to mark the task as completed
-    result_str = serialize_result(result)
-    try:
-        await _update_tool_message(
-            session_id=task.session_id,
-            tool_call_id=task.tool_call_id,
-            content=result_str,
-        )
-    except ToolMessageUpdateError:
-        # DB update failed - mark task as failed to avoid inconsistent state
-        logger.error(
-            f"[COMPLETION] DB update failed for task {task.task_id}, "
-            "marking as failed instead of completed"
-        )
-        await stream_registry.publish_chunk(
-            task.task_id,
-            StreamError(errorText="Failed to save operation result to database"),
-        )
-        await stream_registry.mark_task_completed(task.task_id, status="failed")
-        raise
-
-    # Generate LLM continuation with streaming
-    try:
-        await chat_service._generate_llm_continuation_with_streaming(
-            session_id=task.session_id,
-            user_id=task.user_id,
-            task_id=task.task_id,
-        )
-    except Exception as e:
-        logger.error(
-            f"[COMPLETION] Failed to generate LLM continuation: {e}",
-            exc_info=True,
-        )
-
-    # Mark task as completed and release Redis lock
-    await stream_registry.mark_task_completed(task.task_id, status="completed")
-    try:
-        await chat_service._mark_operation_completed(task.tool_call_id)
-    except Exception as e:
-        logger.error(f"[COMPLETION] Failed to mark operation completed: {e}")
-
-    logger.info(
-        f"[COMPLETION] Successfully processed completion for task {task.task_id}"
-    )
-
-
-async def process_operation_failure(
-    task: stream_registry.ActiveTask,
-    error: str | None,
-) -> None:
-    """Handle failed operation completion.
-
-    Publishes the error to the stream registry, updates the database
-    with the error response, and marks the task as failed.
-
-    Args:
-        task: The active task that failed
-        error: The error message from the operation
-    """
-    error_msg = error or "Operation failed"
-
-    # Publish error to stream registry
-    await stream_registry.publish_chunk(
-        task.task_id,
-        StreamError(errorText=error_msg),
-    )
-
-    # Update pending operation with error
-    # If this fails, we still continue to mark the task as failed
-    error_response = ErrorResponse(
-        message=error_msg,
-        error=error,
-    )
-    try:
-        await _update_tool_message(
-            session_id=task.session_id,
-            tool_call_id=task.tool_call_id,
-            content=error_response.model_dump_json(),
-        )
-    except ToolMessageUpdateError:
-        # DB update failed - log but continue with cleanup
-        logger.error(
-            f"[COMPLETION] DB update failed while processing failure for task {task.task_id}, "
-            "continuing with cleanup"
-        )
-
-    # Mark task as failed and release Redis lock
-    await stream_registry.mark_task_completed(task.task_id, status="failed")
-    try:
-        await chat_service._mark_operation_completed(task.tool_call_id)
-    except Exception as e:
-        logger.error(f"[COMPLETION] Failed to mark operation completed: {e}")
-
-    logger.info(f"[COMPLETION] Processed failure for task {task.task_id}: {error_msg}")

autogpt_platform/backend/backend/copilot/config.py

@@ -36,14 +36,6 @@ class ChatConfig(BaseSettings):
         default=30, description="Maximum number of agent schedules"
     )
 
-    # Long-running operation configuration
-    long_running_operation_ttl: int = Field(
-        default=3600,
-        description="TTL in seconds for long-running operation deduplication lock "
-        "(1 hour, matches stream_ttl). Prevents duplicate operations if pod dies. "
-        "For longer operations, the stream_registry heartbeat keeps them alive.",
-    )
-
     # Stream registry configuration for SSE reconnection
     stream_ttl: int = Field(
         default=3600,
@@ -59,36 +51,14 @@ class ChatConfig(BaseSettings):
         description="Maximum number of messages to store per stream",
     )
 
-    # Redis Streams configuration for completion consumer
-    stream_completion_name: str = Field(
-        default="chat:completions",
-        description="Redis Stream name for operation completions",
-    )
-    stream_consumer_group: str = Field(
-        default="chat_consumers",
-        description="Consumer group name for completion stream",
-    )
-    stream_claim_min_idle_ms: int = Field(
-        default=60000,
-        description="Minimum idle time in milliseconds before claiming pending messages from dead consumers",
-    )
-
     # Redis key prefixes for stream registry
-    task_meta_prefix: str = Field(
+    session_meta_prefix: str = Field(
         default="chat:task:meta:",
-        description="Prefix for task metadata hash keys",
+        description="Prefix for session metadata hash keys",
     )
-    task_stream_prefix: str = Field(
+    turn_stream_prefix: str = Field(
         default="chat:stream:",
-        description="Prefix for task message stream keys",
-    )
-    task_op_prefix: str = Field(
-        default="chat:task:op:",
-        description="Prefix for operation ID to task ID mapping keys",
-    )
-    internal_api_key: str | None = Field(
-        default=None,
-        description="API key for internal webhook callbacks (env: CHAT_INTERNAL_API_KEY)",
+        description="Prefix for turn message stream keys",
     )
 
     # Langfuse Prompt Management Configuration
@@ -160,14 +130,6 @@ class ChatConfig(BaseSettings):
                 v = "https://openrouter.ai/api/v1"
         return v
 
-    @field_validator("internal_api_key", mode="before")
-    @classmethod
-    def get_internal_api_key(cls, v):
-        """Get internal API key from environment if not provided."""
-        if v is None:
-            v = os.getenv("CHAT_INTERNAL_API_KEY")
-        return v
-
     @field_validator("use_claude_agent_sdk", mode="before")
     @classmethod
     def get_use_claude_agent_sdk(cls, v):

autogpt_platform/backend/backend/copilot/executor/manager.py

@@ -25,7 +25,7 @@ from backend.util.process import AppProcess
 from backend.util.retry import continuous_retry
 from backend.util.settings import Settings
 
-from .processor import execute_copilot_task, init_worker
+from .processor import execute_copilot_turn, init_worker
 from .utils import (
     COPILOT_CANCEL_QUEUE_NAME,
     COPILOT_EXECUTION_QUEUE_NAME,
@@ -181,13 +181,13 @@ class CoPilotExecutor(AppProcess):
             self._executor.shutdown(wait=False)
 
         # Release any remaining locks
-        for task_id, lock in list(self._task_locks.items()):
+        for session_id, lock in list(self._task_locks.items()):
             try:
                 lock.release()
-                logger.info(f"[cleanup {pid}] Released lock for {task_id}")
+                logger.info(f"[cleanup {pid}] Released lock for {session_id}")
             except Exception as e:
                 logger.error(
-                    f"[cleanup {pid}] Failed to release lock for {task_id}: {e}"
+                    f"[cleanup {pid}] Failed to release lock for {session_id}: {e}"
                 )
 
         logger.info(f"[cleanup {pid}] Graceful shutdown completed")
@@ -267,20 +267,20 @@ class CoPilotExecutor(AppProcess):
     ):
         """Handle cancel message from FANOUT exchange."""
         request = CancelCoPilotEvent.model_validate_json(body)
-        task_id = request.task_id
-        if not task_id:
-            logger.warning("Cancel message missing 'task_id'")
+        session_id = request.session_id
+        if not session_id:
+            logger.warning("Cancel message missing 'session_id'")
             return
-        if task_id not in self.active_tasks:
-            logger.debug(f"Cancel received for {task_id} but not active")
+        if session_id not in self.active_tasks:
+            logger.debug(f"Cancel received for {session_id} but not active")
             return
 
-        _, cancel_event = self.active_tasks[task_id]
-        logger.info(f"Received cancel for {task_id}")
+        _, cancel_event = self.active_tasks[session_id]
+        logger.info(f"Received cancel for {session_id}")
         if not cancel_event.is_set():
             cancel_event.set()
         else:
-            logger.debug(f"Cancel already set for {task_id}")
+            logger.debug(f"Cancel already set for {session_id}")
 
     def _handle_run_message(
         self,
@@ -352,12 +352,12 @@ class CoPilotExecutor(AppProcess):
             ack_message(reject=True, requeue=False)
             return
 
-        task_id = entry.task_id
+        session_id = entry.session_id
 
-        # Check for local duplicate - task is already running on this executor
-        if task_id in self.active_tasks:
+        # Check for local duplicate - session is already running on this executor
+        if session_id in self.active_tasks:
             logger.warning(
-                f"Task {task_id} already running locally, rejecting duplicate"
+                f"Session {session_id} already running locally, rejecting duplicate"
             )
             ack_message(reject=True, requeue=False)
             return
@@ -365,53 +365,53 @@ class CoPilotExecutor(AppProcess):
         # Try to acquire cluster-wide lock
         cluster_lock = ClusterLock(
             redis=redis.get_redis(),
-            key=f"copilot:task:{task_id}:lock",
+            key=f"copilot:session:{session_id}:lock",
             owner_id=self.executor_id,
             timeout=settings.config.cluster_lock_timeout,
         )
         current_owner = cluster_lock.try_acquire()
         if current_owner != self.executor_id:
             if current_owner is not None:
-                logger.warning(f"Task {task_id} already running on pod {current_owner}")
+                logger.warning(
+                    f"Session {session_id} already running on pod {current_owner}"
+                )
                 ack_message(reject=True, requeue=False)
             else:
                 logger.warning(
-                    f"Could not acquire lock for {task_id} - Redis unavailable"
+                    f"Could not acquire lock for {session_id} - Redis unavailable"
                 )
                 ack_message(reject=True, requeue=True)
             return
 
         # Execute the task
         try:
-            self._task_locks[task_id] = cluster_lock
+            self._task_locks[session_id] = cluster_lock
 
             logger.info(
-                f"Acquired cluster lock for {task_id}, executor_id={self.executor_id}"
+                f"Acquired cluster lock for {session_id}, "
+                f"executor_id={self.executor_id}"
             )
 
             cancel_event = threading.Event()
             future = self.executor.submit(
-                execute_copilot_task, entry, cancel_event, cluster_lock
+                execute_copilot_turn, entry, cancel_event, cluster_lock
             )
-            self.active_tasks[task_id] = (future, cancel_event)
+            self.active_tasks[session_id] = (future, cancel_event)
         except Exception as e:
-            logger.warning(f"Failed to setup execution for {task_id}: {e}")
+            logger.warning(f"Failed to setup execution for {session_id}: {e}")
             cluster_lock.release()
-            if task_id in self._task_locks:
-                del self._task_locks[task_id]
+            if session_id in self._task_locks:
+                del self._task_locks[session_id]
             ack_message(reject=True, requeue=True)
             return
 
         self._update_metrics()
 
         def on_run_done(f: Future):
-            logger.info(f"Run completed for {task_id}")
+            logger.info(f"Run completed for {session_id}")
             try:
                 if exec_error := f.exception():
-                    logger.error(f"Execution for {task_id} failed: {exec_error}")
-                    # Don't requeue failed tasks - they've been marked as failed
-                    # in the stream registry. Requeuing would cause infinite retries
-                    # for deterministic failures.
+                    logger.error(f"Execution for {session_id} failed: {exec_error}")
                     ack_message(reject=True, requeue=False)
                 else:
                     ack_message(reject=False, requeue=False)
@@ -419,10 +419,10 @@ class CoPilotExecutor(AppProcess):
                 logger.exception(f"Error in run completion callback: {e}")
             finally:
                 # Release the cluster lock
-                if task_id in self._task_locks:
-                    logger.info(f"Releasing cluster lock for {task_id}")
-                    self._task_locks[task_id].release()
-                    del self._task_locks[task_id]
+                if session_id in self._task_locks:
+                    logger.info(f"Releasing cluster lock for {session_id}")
+                    self._task_locks[session_id].release()
+                    del self._task_locks[session_id]
                 self._cleanup_completed_tasks()
 
         future.add_done_callback(on_run_done)
@@ -433,11 +433,11 @@ class CoPilotExecutor(AppProcess):
         """Remove completed futures from active_tasks and update metrics."""
         completed_tasks = []
         with self._active_tasks_lock:
-            for task_id, (future, _) in list(self.active_tasks.items()):
+            for session_id, (future, _) in list(self.active_tasks.items()):
                 if future.done():
-                    completed_tasks.append(task_id)
-                    self.active_tasks.pop(task_id, None)
-                    logger.info(f"Cleaned up completed task {task_id}")
+                    completed_tasks.append(session_id)
+                    self.active_tasks.pop(session_id, None)
+                    logger.info(f"Cleaned up completed session {session_id}")
 
         self._update_metrics()
         return completed_tasks

autogpt_platform/backend/backend/copilot/executor/processor.py

@@ -1,6 +1,6 @@
 """CoPilot execution processor - per-worker execution logic.
 
-This module contains the processor class that handles CoPilot task execution
+This module contains the processor class that handles CoPilot session execution
 in a thread-local context, following the graph executor pattern.
 """
 
@@ -12,7 +12,7 @@ import time
 from backend.copilot import service as copilot_service
 from backend.copilot import stream_registry
 from backend.copilot.config import ChatConfig
-from backend.copilot.response_model import StreamError, StreamFinish, StreamFinishStep
+from backend.copilot.response_model import StreamFinish
 from backend.copilot.sdk import service as sdk_service
 from backend.executor.cluster_lock import ClusterLock
 from backend.util.decorator import error_logged
@@ -32,17 +32,17 @@ logger = TruncatedLogger(logging.getLogger(__name__), prefix="[CoPilotExecutor]"
 _tls = threading.local()
 
 
-def execute_copilot_task(
+def execute_copilot_turn(
     entry: CoPilotExecutionEntry,
     cancel: threading.Event,
     cluster_lock: ClusterLock,
 ):
-    """Execute a CoPilot task using the thread-local processor.
+    """Execute a single CoPilot turn (user message → AI response).
 
     This function is the entry point called by the thread pool executor.
 
     Args:
-        entry: The task payload
+        entry: The turn payload
         cancel: Threading event to signal cancellation
         cluster_lock: Distributed lock for this execution
     """
@@ -76,16 +76,16 @@ def cleanup_worker():
 
 
 class CoPilotProcessor:
-    """Per-worker execution logic for CoPilot tasks.
+    """Per-worker execution logic for CoPilot sessions.
 
     This class is instantiated once per worker thread and handles the execution
-    of CoPilot chat generation tasks. It maintains an async event loop for
+    of CoPilot chat generation sessions. It maintains an async event loop for
     running the async service code.
 
     The execution flow:
-        1. CoPilot task is picked from RabbitMQ queue
-        2. Manager submits task to thread pool
-        3. Processor executes the task in its event loop
+        1. Session entry is picked from RabbitMQ queue
+        2. Manager submits to thread pool
+        3. Processor executes in its event loop
         4. Results are published to Redis Streams
     """
 
@@ -139,19 +139,17 @@ class CoPilotProcessor:
         cancel: threading.Event,
         cluster_lock: ClusterLock,
     ):
-        """Execute a CoPilot task.
+        """Execute a CoPilot turn.
 
-        This is the main entry point for task execution. It runs the async
-        execution logic in the worker's event loop and handles errors.
+        Runs the async logic in the worker's event loop and handles errors.
 
         Args:
-            entry: The task payload containing session and message info
+            entry: The turn payload containing session and message info
             cancel: Threading event to signal cancellation
             cluster_lock: Distributed lock to prevent duplicate execution
         """
         log = CoPilotLogMetadata(
             logging.getLogger(__name__),
-            task_id=entry.task_id,
             session_id=entry.session_id,
             user_id=entry.user_id,
         )
@@ -185,11 +183,20 @@ class CoPilotProcessor:
             elapsed = time.monotonic() - start_time
             log.info(f"Execution completed in {elapsed:.2f}s")
 
-        except Exception as e:
+        except BaseException as e:
             elapsed = time.monotonic() - start_time
             log.error(f"Execution failed after {elapsed:.2f}s: {e}")
-            # Note: _execute_async already marks the task as failed before re-raising,
-            # so we don't call _mark_task_failed here to avoid duplicate error events.
+            # Safety net: if _execute_async's error handler failed to mark
+            # the session (e.g. RuntimeError from SDK cleanup), do it here.
+            try:
+                asyncio.run_coroutine_threadsafe(
+                    stream_registry.mark_session_completed(
+                        entry.session_id, error_message=str(e) or "Unknown error"
+                    ),
+                    self.execution_loop,
+                ).result(timeout=5.0)
+            except Exception as cleanup_err:
+                log.error(f"Safety net mark_session_completed failed: {cleanup_err}")
             raise
 
     async def _execute_async(
@@ -199,16 +206,16 @@ class CoPilotProcessor:
         cluster_lock: ClusterLock,
         log: CoPilotLogMetadata,
     ):
-        """Async execution logic for CoPilot task.
+        """Async execution logic for a CoPilot turn.
 
-        This method calls the existing stream_chat_completion service function
-        and publishes results to the stream registry.
+        Calls the stream_chat_completion service function and publishes
+        results to the stream registry.
 
         Args:
-            entry: The task payload
+            entry: The turn payload
             cancel: Threading event to signal cancellation
             cluster_lock: Distributed lock for refresh
-            log: Structured logger for this task
+            log: Structured logger
         """
         last_refresh = time.monotonic()
         refresh_interval = 30.0  # Refresh lock every 30 seconds
@@ -228,7 +235,7 @@ class CoPilotProcessor:
             )
             log.info(f"Using {'SDK' if use_sdk else 'standard'} service")
 
-            # Stream chat completion and publish chunks to Redis
+            # Stream chat completion and publish chunks to Redis.
             async for chunk in stream_fn(
                 session_id=entry.session_id,
                 message=entry.message if entry.message else None,
@@ -236,56 +243,38 @@ class CoPilotProcessor:
                 user_id=entry.user_id,
                 context=entry.context,
             ):
-                # Check for cancellation
                 if cancel.is_set():
-                    log.info("Cancelled during streaming")
-                    await stream_registry.publish_chunk(
-                        entry.task_id, StreamError(errorText="Operation cancelled")
-                    )
-                    await stream_registry.publish_chunk(
-                        entry.task_id, StreamFinishStep()
-                    )
-                    await stream_registry.publish_chunk(entry.task_id, StreamFinish())
-                    await stream_registry.mark_task_completed(
-                        entry.task_id, status="failed"
-                    )
-                    return
+                    log.info("Cancel requested, breaking stream")
+                    break
 
-                # Refresh cluster lock periodically
                 current_time = time.monotonic()
                 if current_time - last_refresh >= refresh_interval:
                     cluster_lock.refresh()
                     last_refresh = current_time
 
-                # Publish chunk to stream registry
-                await stream_registry.publish_chunk(entry.task_id, chunk)
+                # Skip StreamFinish — mark_session_completed publishes it.
+                if isinstance(chunk, StreamFinish):
+                    continue
 
-            # Mark task as completed
-            await stream_registry.mark_task_completed(entry.task_id, status="completed")
-            log.info("Task completed successfully")
+                try:
+                    await stream_registry.publish_chunk(entry.turn_id, chunk)
+                except Exception as e:
+                    log.error(
+                        f"Error publishing chunk {type(chunk).__name__}: {e}",
+                        exc_info=True,
+                    )
 
-        except asyncio.CancelledError:
-            log.info("Task cancelled")
-            await stream_registry.mark_task_completed(
-                entry.task_id,
-                status="failed",
-                error_message="Task was cancelled",
+            error_message = "Operation cancelled" if cancel.is_set() else None
+            await stream_registry.mark_session_completed(
+                entry.session_id, error_message=error_message
             )
-            raise
 
-        except Exception as e:
-            log.error(f"Task failed: {e}")
-            await self._mark_task_failed(entry.task_id, str(e))
+        except BaseException as e:
+            log.error(f"Turn failed: {e}")
+            try:
+                await stream_registry.mark_session_completed(
+                    entry.session_id, error_message=str(e) or "Unknown error"
+                )
+            except Exception as mark_err:
+                log.error(f"mark_session_completed also failed: {mark_err}")
             raise
-
-    async def _mark_task_failed(self, task_id: str, error_message: str):
-        """Mark a task as failed and publish error to stream registry."""
-        try:
-            await stream_registry.publish_chunk(
-                task_id, StreamError(errorText=error_message)
-            )
-            await stream_registry.publish_chunk(task_id, StreamFinishStep())
-            await stream_registry.publish_chunk(task_id, StreamFinish())
-            await stream_registry.mark_task_completed(task_id, status="failed")
-        except Exception as e:
-            logger.error(f"Failed to mark task {task_id} as failed: {e}")

autogpt_platform/backend/backend/copilot/executor/utils.py

@@ -28,7 +28,7 @@ class CoPilotLogMetadata(TruncatedLogger):
     Args:
         logger: The underlying logger instance
         max_length: Maximum log message length before truncation
-        **kwargs: Metadata key-value pairs (e.g., task_id="abc", session_id="xyz")
+        **kwargs: Metadata key-value pairs (e.g., session_id="xyz", turn_id="abc")
             These are added to json_fields in cloud mode, or to the prefix in local mode.
     """
 
@@ -135,18 +135,15 @@ class CoPilotExecutionEntry(BaseModel):
     This model represents a chat generation task to be processed by the executor.
     """
 
-    task_id: str
-    """Unique identifier for this task (used for stream registry)"""
-
     session_id: str
-    """Chat session ID"""
+    """Chat session ID (also used for dedup/locking)"""
+
+    turn_id: str = ""
+    """Per-turn UUID for Redis stream isolation"""
 
     user_id: str | None
     """User ID (may be None for anonymous users)"""
 
-    operation_id: str
-    """Operation ID for webhook callbacks and completion tracking"""
-
     message: str
     """User's message to process"""
 
@@ -160,40 +157,37 @@ class CoPilotExecutionEntry(BaseModel):
 class CancelCoPilotEvent(BaseModel):
     """Event to cancel a CoPilot operation."""
 
-    task_id: str
-    """Task ID to cancel"""
+    session_id: str
+    """Session ID to cancel"""
 
 
 # ============ Queue Publishing Helpers ============ #
 
 
-async def enqueue_copilot_task(
-    task_id: str,
+async def enqueue_copilot_turn(
     session_id: str,
     user_id: str | None,
-    operation_id: str,
     message: str,
+    turn_id: str,
     is_user_message: bool = True,
     context: dict[str, str] | None = None,
 ) -> None:
     """Enqueue a CoPilot task for processing by the executor service.
 
     Args:
-        task_id: Unique identifier for this task (used for stream registry)
-        session_id: Chat session ID
+        session_id: Chat session ID (also used for dedup/locking)
         user_id: User ID (may be None for anonymous users)
-        operation_id: Operation ID for webhook callbacks and completion tracking
         message: User's message to process
+        turn_id: Per-turn UUID for Redis stream isolation
         is_user_message: Whether the message is from the user (vs system/assistant)
         context: Optional context for the message (e.g., {url: str, content: str})
     """
     from backend.util.clients import get_async_copilot_queue
 
     entry = CoPilotExecutionEntry(
-        task_id=task_id,
         session_id=session_id,
+        turn_id=turn_id,
         user_id=user_id,
-        operation_id=operation_id,
         message=message,
         is_user_message=is_user_message,
         context=context,
@@ -207,15 +201,15 @@ async def enqueue_copilot_task(
     )
 
 
-async def enqueue_cancel_task(task_id: str) -> None:
-    """Publish a cancel request for a running CoPilot task.
+async def enqueue_cancel_task(session_id: str) -> None:
+    """Publish a cancel request for a running CoPilot session.
 
     Sends a ``CancelCoPilotEvent`` to the FANOUT exchange so all executor
     pods receive the cancellation signal.
     """
     from backend.util.clients import get_async_copilot_queue
 
-    event = CancelCoPilotEvent(task_id=task_id)
+    event = CancelCoPilotEvent(session_id=session_id)
     queue_client = await get_async_copilot_queue()
     await queue_client.publish_message(
         routing_key="",  # FANOUT ignores routing key

autogpt_platform/backend/backend/copilot/parallel_tool_calls_test.py

@@ -14,7 +14,6 @@ import pytest
 @pytest.mark.asyncio
 async def test_parallel_tool_calls_run_concurrently():
     """Multiple tool calls should complete in ~max(delays), not sum(delays)."""
-    # Import here to allow module-level mocking if needed
     from backend.copilot.response_model import (
         StreamToolInputAvailable,
         StreamToolOutputAvailable,
@@ -32,7 +31,6 @@ async def test_parallel_tool_calls_run_concurrently():
         for i in range(n_tools)
     ]
 
-    # Minimal session mock
     class FakeSession:
         session_id = "test"
         user_id = "test"
@@ -42,7 +40,7 @@ async def test_parallel_tool_calls_run_concurrently():
 
     original_yield = None
 
-    async def fake_yield(tc_list, idx, sess, lock=None):
+    async def fake_yield(tc_list, idx, sess):
         yield StreamToolInputAvailable(
             toolCallId=tc_list[idx]["id"],
             toolName=tc_list[idx]["function"]["name"],
@@ -101,7 +99,7 @@ async def test_single_tool_call_works():
         def __init__(self):
             self.messages = []
 
-    async def fake_yield(tc_list, idx, sess, lock=None):
+    async def fake_yield(tc_list, idx, sess):
         yield StreamToolInputAvailable(toolCallId="call_0", toolName="t", input={})
         yield StreamToolOutputAvailable(toolCallId="call_0", toolName="t", output="{}")
 
@@ -144,7 +142,7 @@ async def test_retryable_error_propagates():
         def __init__(self):
             self.messages = []
 
-    async def fake_yield(tc_list, idx, sess, lock=None):
+    async def fake_yield(tc_list, idx, sess):
         if idx == 1:
             raise KeyError("bad")
         from backend.copilot.response_model import StreamToolInputAvailable
@@ -175,8 +173,8 @@ async def test_retryable_error_propagates():
 
 
 @pytest.mark.asyncio
-async def test_session_lock_shared():
-    """All parallel tools should receive the same lock instance."""
+async def test_session_shared_across_parallel_tools():
+    """All parallel tools should receive the same session instance."""
     from backend.copilot.response_model import (
         StreamToolInputAvailable,
         StreamToolOutputAvailable,
@@ -199,10 +197,10 @@ async def test_session_lock_shared():
         def __init__(self):
             self.messages = []
 
-    observed_locks = []
+    observed_sessions = []
 
-    async def fake_yield(tc_list, idx, sess, lock=None):
-        observed_locks.append(lock)
+    async def fake_yield(tc_list, idx, sess):
+        observed_sessions.append(sess)
         yield StreamToolInputAvailable(
             toolCallId=tc_list[idx]["id"], toolName=f"t_{idx}", input={}
         )
@@ -222,9 +220,8 @@ async def test_session_lock_shared():
     finally:
         svc._yield_tool_call = orig
 
-    assert len(observed_locks) == 3
-    assert observed_locks[0] is observed_locks[1] is observed_locks[2]
-    assert isinstance(observed_locks[0], asyncio.Lock)
+    assert len(observed_sessions) == 3
+    assert observed_sessions[0] is observed_sessions[1] is observed_sessions[2]
 
 
 @pytest.mark.asyncio
@@ -251,7 +248,7 @@ async def test_cancellation_cleans_up():
 
     started = asyncio.Event()
 
-    async def fake_yield(tc_list, idx, sess, lock=None):
+    async def fake_yield(tc_list, idx, sess):
         yield StreamToolInputAvailable(
             toolCallId=tc_list[idx]["id"], toolName=f"t_{idx}", input={}
         )

autogpt_platform/backend/backend/copilot/response_model.py

@@ -5,6 +5,8 @@ This module implements the AI SDK UI Stream Protocol (v1) for streaming chat res
 See: https://ai-sdk.dev/docs/ai-sdk-ui/stream-protocol
 """
 
+import json
+import logging
 from enum import Enum
 from typing import Any
 
@@ -12,6 +14,8 @@ from pydantic import BaseModel, Field
 
 from backend.util.json import dumps as json_dumps
 
+logger = logging.getLogger(__name__)
+
 
 class ResponseType(str, Enum):
     """Types of streaming responses following AI SDK protocol."""
@@ -47,7 +51,8 @@ class StreamBaseResponse(BaseModel):
 
     def to_sse(self) -> str:
         """Convert to SSE format."""
-        return f"data: {self.model_dump_json()}\n\n"
+        json_str = self.model_dump_json(exclude_none=True)
+        return f"data: {json_str}\n\n"
 
 
 # ========== Message Lifecycle ==========
@@ -58,15 +63,13 @@ class StreamStart(StreamBaseResponse):
 
     type: ResponseType = ResponseType.START
     messageId: str = Field(..., description="Unique message ID")
-    taskId: str | None = Field(
+    sessionId: str | None = Field(
         default=None,
-        description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
+        description="Session ID for SSE reconnection.",
     )
 
     def to_sse(self) -> str:
-        """Convert to SSE format, excluding non-protocol fields like taskId."""
-        import json
-
+        """Convert to SSE format, excluding non-protocol fields like sessionId."""
         data: dict[str, Any] = {
             "type": self.type.value,
             "messageId": self.messageId,
@@ -163,8 +166,6 @@ class StreamToolOutputAvailable(StreamBaseResponse):
 
     def to_sse(self) -> str:
         """Convert to SSE format, excluding non-spec fields."""
-        import json
-
         data = {
             "type": self.type.value,
             "toolCallId": self.toolCallId,

autogpt_platform/backend/backend/copilot/sdk/dummy.py

@@ -0,0 +1,57 @@
+"""Dummy SDK service for testing copilot streaming.
+
+Returns mock streaming responses without calling Claude Agent SDK.
+Enable via COPILOT_TEST_MODE=true environment variable.
+
+WARNING: This is for testing only. Do not use in production.
+"""
+
+import asyncio
+import logging
+import uuid
+from collections.abc import AsyncGenerator
+
+from ..model import ChatSession
+from ..response_model import StreamBaseResponse, StreamStart, StreamTextDelta
+
+logger = logging.getLogger(__name__)
+
+
+async def stream_chat_completion_dummy(
+    session_id: str,
+    message: str | None = None,
+    tool_call_response: str | None = None,
+    is_user_message: bool = True,
+    user_id: str | None = None,
+    retry_count: int = 0,
+    session: ChatSession | None = None,
+    context: dict[str, str] | None = None,
+) -> AsyncGenerator[StreamBaseResponse, None]:
+    """Stream dummy chat completion for testing.
+
+    Returns a simple streaming response with text deltas to test:
+    - Streaming infrastructure works
+    - No timeout occurs
+    - Text arrives in chunks
+    - StreamFinish is sent by mark_session_completed
+    """
+    logger.warning(
+        f"[TEST MODE] Using dummy copilot streaming for session {session_id}"
+    )
+
+    message_id = str(uuid.uuid4())
+    text_block_id = str(uuid.uuid4())
+
+    # Start the stream
+    yield StreamStart(messageId=message_id, sessionId=session_id)
+
+    # Simulate streaming text response with delays
+    dummy_response = "I counted: 1... 2... 3. All done!"
+    words = dummy_response.split()
+
+    for i, word in enumerate(words):
+        # Add space except for last word
+        text = word if i == len(words) - 1 else f"{word} "
+        yield StreamTextDelta(id=text_block_id, delta=text)
+        # Small delay to simulate real streaming
+        await asyncio.sleep(0.1)

autogpt_platform/backend/backend/copilot/sdk/response_adapter.py

@@ -55,13 +55,8 @@ class SDKResponseAdapter:
         self.has_ended_text = False
         self.current_tool_calls: dict[str, dict[str, str]] = {}
         self.resolved_tool_calls: set[str] = set()
-        self.task_id: str | None = None
         self.step_open = False
 
-    def set_task_id(self, task_id: str) -> None:
-        """Set the task ID for reconnection support."""
-        self.task_id = task_id
-
     @property
     def has_unresolved_tool_calls(self) -> bool:
         """True when there are tool calls that haven't received output yet."""
@@ -74,7 +69,7 @@ class SDKResponseAdapter:
         if isinstance(sdk_message, SystemMessage):
             if sdk_message.subtype == "init":
                 responses.append(
-                    StreamStart(messageId=self.message_id, taskId=self.task_id)
+                    StreamStart(messageId=self.message_id, sessionId=self.session_id)
                 )
                 # Open the first step (matches non-SDK: StreamStart then StreamStartStep)
                 responses.append(StreamStartStep())

autogpt_platform/backend/backend/copilot/sdk/response_adapter_test.py

@@ -37,9 +37,7 @@ from .tool_adapter import wait_for_stash
 
 
 def _adapter() -> SDKResponseAdapter:
-    a = SDKResponseAdapter(message_id="msg-1")
-    a.set_task_id("task-1")
-    return a
+    return SDKResponseAdapter(message_id="msg-1", session_id="session-1")
 
 
 # -- SystemMessage -----------------------------------------------------------
@@ -51,7 +49,7 @@ def test_system_init_emits_start_and_step():
     assert len(results) == 2
     assert isinstance(results[0], StreamStart)
     assert results[0].messageId == "msg-1"
-    assert results[0].taskId == "task-1"
+    assert results[0].sessionId == "session-1"
     assert isinstance(results[1], StreamStartStep)
 
 

autogpt_platform/backend/backend/copilot/sdk/service.py

@@ -13,7 +13,6 @@ from backend.data.redis_client import get_redis_async
 from backend.executor.cluster_lock import AsyncClusterLock
 from backend.util.exceptions import NotFoundError
 
-from .. import stream_registry
 from ..config import ChatConfig
 from ..model import (
     ChatMessage,
@@ -26,19 +25,13 @@ from ..response_model import (
     StreamBaseResponse,
     StreamError,
     StreamFinish,
-    StreamFinishStep,
     StreamHeartbeat,
     StreamStart,
     StreamTextDelta,
     StreamToolInputAvailable,
     StreamToolOutputAvailable,
 )
-from ..service import (
-    _build_system_prompt,
-    _execute_long_running_tool_with_streaming,
-    _generate_session_title,
-)
-from ..tools.models import OperationPendingResponse, OperationStartedResponse
+from ..service import _build_system_prompt, _generate_session_title
 from ..tools.sandbox import WORKSPACE_PREFIX, make_session_path
 from ..tracking import track_user_message
 from .response_adapter import SDKResponseAdapter
@@ -46,7 +39,6 @@ from .security_hooks import create_security_hooks
 from .tool_adapter import (
     COPILOT_TOOL_NAMES,
     SDK_DISALLOWED_TOOLS,
-    LongRunningCallback,
     create_copilot_mcp_server,
     set_execution_context,
     wait_for_stash,
@@ -84,7 +76,8 @@ class CapturedTranscript:
 _SDK_CWD_PREFIX = WORKSPACE_PREFIX
 
 # Heartbeat interval — keep SSE alive through proxies/LBs during tool execution.
-_HEARTBEAT_INTERVAL = 15.0  # seconds
+# IMPORTANT: Must be less than frontend timeout (12s in useCopilotPage.ts)
+_HEARTBEAT_INTERVAL = 10.0  # seconds
 
 # Appended to the system prompt to inform the agent about available tools.
 # The SDK built-in Bash is NOT available — use mcp__copilot__bash_exec instead,
@@ -138,127 +131,6 @@ is delivered to the user via a background stream.
 STREAM_LOCK_PREFIX = "copilot:stream:lock:"
 
 
-def _build_long_running_callback(
-    user_id: str | None,
-) -> LongRunningCallback:
-    """Build a callback that delegates long-running tools to the non-SDK infrastructure.
-
-    Long-running tools (create_agent, edit_agent, etc.) are delegated to the
-    existing background infrastructure: stream_registry (Redis Streams),
-    database persistence, and SSE reconnection.  This means results survive
-    page refreshes / pod restarts, and the frontend shows the proper loading
-    widget with progress updates.
-
-    Args:
-        user_id: User ID for the session
-
-    The returned callback matches the ``LongRunningCallback`` signature:
-    ``(tool_name, args, session) -> MCP response dict``.
-    """
-
-    async def _callback(
-        tool_name: str, args: dict[str, Any], session: ChatSession
-    ) -> dict[str, Any]:
-        operation_id = str(uuid.uuid4())
-        task_id = str(uuid.uuid4())
-        tool_call_id = f"sdk-{uuid.uuid4().hex[:12]}"
-        session_id = session.session_id
-
-        # --- Build user-friendly messages (matches non-SDK service) ---
-        if tool_name == "create_agent":
-            desc = args.get("description", "")
-            desc_preview = (desc[:100] + "...") if len(desc) > 100 else desc
-            pending_msg = (
-                f"Creating your agent: {desc_preview}"
-                if desc_preview
-                else "Creating agent... This may take a few minutes."
-            )
-            started_msg = (
-                "Agent creation started. You can close this tab - "
-                "check your library in a few minutes."
-            )
-        elif tool_name == "edit_agent":
-            changes = args.get("changes", "")
-            changes_preview = (changes[:100] + "...") if len(changes) > 100 else changes
-            pending_msg = (
-                f"Editing agent: {changes_preview}"
-                if changes_preview
-                else "Editing agent... This may take a few minutes."
-            )
-            started_msg = (
-                "Agent edit started. You can close this tab - "
-                "check your library in a few minutes."
-            )
-        else:
-            pending_msg = f"Running {tool_name}... This may take a few minutes."
-            started_msg = (
-                f"{tool_name} started. You can close this tab - "
-                "check back in a few minutes."
-            )
-
-        # --- Register task in Redis for SSE reconnection ---
-        await stream_registry.create_task(
-            task_id=task_id,
-            session_id=session_id,
-            user_id=user_id,
-            tool_call_id=tool_call_id,
-            tool_name=tool_name,
-            operation_id=operation_id,
-        )
-
-        # --- Save OperationPendingResponse to chat history ---
-        pending_message = ChatMessage(
-            role="tool",
-            content=OperationPendingResponse(
-                message=pending_msg,
-                operation_id=operation_id,
-                tool_name=tool_name,
-            ).model_dump_json(),
-            tool_call_id=tool_call_id,
-        )
-        session.messages.append(pending_message)
-        # Collision detection happens in add_chat_messages_batch (db.py)
-        session = await upsert_chat_session(session)
-
-        # --- Spawn background task (reuses non-SDK infrastructure) ---
-        bg_task = asyncio.create_task(
-            _execute_long_running_tool_with_streaming(
-                tool_name=tool_name,
-                parameters=args,
-                tool_call_id=tool_call_id,
-                operation_id=operation_id,
-                task_id=task_id,
-                session_id=session_id,
-                user_id=user_id,
-            )
-        )
-        _background_tasks.add(bg_task)
-        bg_task.add_done_callback(_background_tasks.discard)
-        await stream_registry.set_task_asyncio_task(task_id, bg_task)
-
-        logger.info(
-            f"[SDK] Long-running tool {tool_name} delegated to background "
-            f"(operation_id={operation_id}, task_id={task_id})"
-        )
-
-        # --- Return OperationStartedResponse as MCP tool result ---
-        # This flows through SDK → response adapter → frontend, triggering
-        # the loading widget with SSE reconnection support.
-        started_json = OperationStartedResponse(
-            message=started_msg,
-            operation_id=operation_id,
-            tool_name=tool_name,
-            task_id=task_id,
-        ).model_dump_json()
-
-        return {
-            "content": [{"type": "text", "text": started_json}],
-            "isError": False,
-        }
-
-    return _callback
-
-
 def _resolve_sdk_model() -> str | None:
     """Resolve the model name for the Claude Agent SDK CLI.
 
@@ -577,8 +449,7 @@ async def stream_chat_completion_sdk(
     )
     system_prompt += _SDK_TOOL_SUPPLEMENT
     message_id = str(uuid.uuid4())
-    task_id = str(uuid.uuid4())
-    stream_id = task_id  # Use task_id as unique stream identifier
+    stream_id = str(uuid.uuid4())
 
     # Acquire stream lock to prevent concurrent streams to the same session
     lock = AsyncClusterLock(
@@ -599,10 +470,9 @@ async def stream_chat_completion_sdk(
             "Please wait or stop it.",
             code="stream_already_active",
         )
-        yield StreamFinish()
         return
 
-    yield StreamStart(messageId=message_id, taskId=task_id)
+    yield StreamStart(messageId=message_id, sessionId=session_id)
 
     stream_completed = False
     # Initialise variables before the try so the finally block can
@@ -618,11 +488,7 @@ async def stream_chat_completion_sdk(
         sdk_cwd = _make_sdk_cwd(session_id)
         os.makedirs(sdk_cwd, exist_ok=True)
 
-        set_execution_context(
-            user_id,
-            session,
-            long_running_callback=_build_long_running_callback(user_id),
-        )
+        set_execution_context(user_id, session)
         try:
             from claude_agent_sdk import ClaudeAgentOptions, ClaudeSDKClient
 
@@ -714,7 +580,6 @@ async def stream_chat_completion_sdk(
             options = ClaudeAgentOptions(**sdk_options_kwargs)  # type: ignore[arg-type]
 
             adapter = SDKResponseAdapter(message_id=message_id, session_id=session_id)
-            adapter.set_task_id(task_id)
 
             async with ClaudeSDKClient(options=options) as client:
                 current_message = message or ""
@@ -728,7 +593,6 @@ async def stream_chat_completion_sdk(
                         errorText="Message cannot be empty.",
                         code="empty_prompt",
                     )
-                    yield StreamFinish()
                     return
 
                 query_message = await _build_query_message(
@@ -739,8 +603,7 @@ async def stream_chat_completion_sdk(
                     session_id,
                 )
                 logger.info(
-                    "[SDK] [%s] Sending query — resume=%s, "
-                    "total_msgs=%d, query_len=%d",
+                    "[SDK] [%s] Sending query — resume=%s, total_msgs=%d, query_len=%d",
                     session_id[:12],
                     use_resume,
                     len(session.messages),
@@ -789,8 +652,7 @@ async def stream_chat_completion_sdk(
                             sdk_msg = done.pop().result()
                         except StopAsyncIteration:
                             logger.info(
-                                "[SDK] [%s] Stream ended normally "
-                                "(StopAsyncIteration)",
+                                "[SDK] [%s] Stream ended normally (StopAsyncIteration)",
                                 session_id[:12],
                             )
                             break
@@ -927,18 +789,6 @@ async def stream_chat_completion_sdk(
                                 if not has_appended_assistant:
                                     session.messages.append(assistant_response)
                                     has_appended_assistant = True
-                                # Save before tool execution starts so the
-                                # pending tool call is visible on refresh /
-                                # other devices. Collision detection happens
-                                # in add_chat_messages_batch (db.py).
-                                try:
-                                    session = await upsert_chat_session(session)
-                                except Exception as save_err:
-                                    logger.warning(
-                                        "[SDK] [%s] Incremental save " "failed: %s",
-                                        session_id[:12],
-                                        save_err,
-                                    )
 
                             elif isinstance(response, StreamToolOutputAvailable):
                                 session.messages.append(
@@ -953,17 +803,6 @@ async def stream_chat_completion_sdk(
                                     )
                                 )
                                 has_tool_results = True
-                                # Save after tool completes so the result is
-                                # visible on refresh / other devices.
-                                # Collision detection happens in add_chat_messages_batch (db.py).
-                                try:
-                                    session = await upsert_chat_session(session)
-                                except Exception as save_err:
-                                    logger.warning(
-                                        "[SDK] [%s] Incremental save " "failed: %s",
-                                        session_id[:12],
-                                        save_err,
-                                    )
 
                             elif isinstance(response, StreamFinish):
                                 stream_completed = True
@@ -973,8 +812,7 @@ async def stream_chat_completion_sdk(
                     # server shutdown).  Log and let the safety-net / finally
                     # blocks handle cleanup.
                     logger.warning(
-                        "[SDK] [%s] Streaming loop cancelled "
-                        "(asyncio.CancelledError)",
+                        "[SDK] [%s] Streaming loop cancelled (asyncio.CancelledError)",
                         session_id[:12],
                     )
                     raise
@@ -1016,25 +854,20 @@ async def stream_chat_completion_sdk(
                             )
                         yield response
 
-                # If the stream ended without a ResultMessage (no
-                # StreamFinish), the SDK CLI exited unexpectedly.  Close
-                # the open step and emit StreamFinish so the frontend
-                # transitions to the "ready" state.
+                # If the stream ended without a ResultMessage, the SDK
+                # CLI exited unexpectedly.  Close any open text/step so
+                # the chunks are well-formed.  StreamFinish is published
+                # by mark_session_completed in the processor.
                 if not stream_completed:
                     logger.warning(
                         "[SDK] [%s] Stream ended without ResultMessage "
-                        "(StopAsyncIteration) — emitting StreamFinish",
+                        "(StopAsyncIteration)",
                         session_id[:12],
                     )
-                    if adapter.step_open:
-                        yield StreamFinishStep()
-                        adapter.step_open = False
                     closing_responses: list[StreamBaseResponse] = []
                     adapter._end_text_if_open(closing_responses)
                     for r in closing_responses:
                         yield r
-                    yield StreamFinish()
-                    stream_completed = True
 
                 if (
                     assistant_response.content or assistant_response.tool_calls
@@ -1054,7 +887,7 @@ async def stream_chat_completion_sdk(
                 elif captured_transcript.path:
                     raw_transcript = read_transcript_file(captured_transcript.path)
                     logger.debug(
-                        "[SDK] Transcript source: stop hook (%s), " "read result: %s",
+                        "[SDK] Transcript source: stop hook (%s), read result: %s",
                         captured_transcript.path,
                         f"{len(raw_transcript)}B" if raw_transcript else "None",
                     )
@@ -1095,14 +928,24 @@ async def stream_chat_completion_sdk(
             session_id[:12],
             len(session.messages),
         )
-        if not stream_completed:
-            yield StreamFinish()
-
     except asyncio.CancelledError:
-        # Client disconnect / server shutdown — log but re-raise so
-        # the framework can clean up.  The finally block still runs
-        # for transcript upload.
+        # Client disconnect / server shutdown — save session before re-raising
+        # so accumulated messages aren't lost.
         logger.warning("[SDK] [%s] Session cancelled (CancelledError)", session_id[:12])
+        if session:
+            try:
+                await asyncio.shield(upsert_chat_session(session))
+                logger.info(
+                    "[SDK] [%s] Session saved on cancel (%d messages)",
+                    session_id[:12],
+                    len(session.messages),
+                )
+            except Exception as save_err:
+                logger.error(
+                    "[SDK] [%s] Failed to save session on cancel: %s",
+                    session_id[:12],
+                    save_err,
+                )
         raise
     except Exception as e:
         logger.error(f"[SDK] Error: {e}", exc_info=True)
@@ -1115,7 +958,6 @@ async def stream_chat_completion_sdk(
             errorText="An error occurred. Please try again.",
             code="sdk_error",
         )
-        yield StreamFinish()
     finally:
         # --- Upload transcript for next-turn --resume ---
         # This MUST run in finally so the transcript is uploaded even when

autogpt_platform/backend/backend/copilot/sdk/tool_adapter.py

@@ -2,11 +2,6 @@
 
 This module provides the adapter layer that converts existing BaseTool implementations
 into in-process MCP tools that can be used with the Claude Agent SDK.
-
-Long-running tools (``is_long_running=True``) are delegated to the non-SDK
-background infrastructure (stream_registry, Redis persistence, SSE reconnection)
-via a callback provided by the service layer.  This avoids wasteful SDK polling
-and makes results survive page refreshes.
 """
 
 import asyncio
@@ -15,7 +10,6 @@ import json
 import logging
 import os
 import uuid
-from collections.abc import Awaitable, Callable
 from contextvars import ContextVar
 from typing import Any
 
@@ -43,7 +37,8 @@ _current_session: ContextVar[ChatSession | None] = ContextVar(
 # Keyed by tool_name → full output string. Consumed (popped) by the
 # response adapter when it builds StreamToolOutputAvailable.
 _pending_tool_outputs: ContextVar[dict[str, list[str]]] = ContextVar(
-    "pending_tool_outputs", default=None  # type: ignore[arg-type]
+    "pending_tool_outputs",
+    default=None,  # type: ignore[arg-type]
 )
 # Event signaled whenever stash_pending_tool_output() adds a new entry.
 # Used by the streaming loop to wait for PostToolUse hooks to complete
@@ -54,22 +49,10 @@ _stash_event: ContextVar[asyncio.Event | None] = ContextVar(
     "_stash_event", default=None
 )
 
-# Callback type for delegating long-running tools to the non-SDK infrastructure.
-# Args: (tool_name, arguments, session) → MCP-formatted response dict.
-LongRunningCallback = Callable[
-    [str, dict[str, Any], ChatSession], Awaitable[dict[str, Any]]
-]
-
-# ContextVar so the service layer can inject the callback per-request.
-_long_running_callback: ContextVar[LongRunningCallback | None] = ContextVar(
-    "long_running_callback", default=None
-)
-
 
 def set_execution_context(
     user_id: str | None,
     session: ChatSession,
-    long_running_callback: LongRunningCallback | None = None,
 ) -> None:
     """Set the execution context for tool calls.
 
@@ -79,14 +62,11 @@ def set_execution_context(
     Args:
         user_id: Current user's ID.
         session: Current chat session.
-        long_running_callback: Optional callback to delegate long-running tools
-            to the non-SDK background infrastructure (stream_registry + Redis).
     """
     _current_user_id.set(user_id)
     _current_session.set(session)
     _pending_tool_outputs.set({})
     _stash_event.set(asyncio.Event())
-    _long_running_callback.set(long_running_callback)
 
 
 def get_execution_context() -> tuple[str | None, ChatSession | None]:
@@ -276,11 +256,6 @@ def create_tool_handler(base_tool: BaseTool):
 
     This wraps the existing BaseTool._execute method to be compatible
     with the Claude Agent SDK MCP tool format.
-
-    Long-running tools (``is_long_running=True``) are delegated to the
-    non-SDK background infrastructure via a callback set in the execution
-    context.  The callback persists the operation in Redis (stream_registry)
-    so results survive page refreshes and pod restarts.
     """
 
     async def tool_handler(args: dict[str, Any]) -> dict[str, Any]:
@@ -290,25 +265,6 @@ def create_tool_handler(base_tool: BaseTool):
         if session is None:
             return _mcp_error("No session context available")
 
-        # --- Long-running: delegate to non-SDK background infrastructure ---
-        if base_tool.is_long_running:
-            callback = _long_running_callback.get(None)
-            if callback:
-                try:
-                    return await callback(base_tool.name, args, session)
-                except Exception as e:
-                    logger.error(
-                        f"Long-running callback failed for {base_tool.name}: {e}",
-                        exc_info=True,
-                    )
-                    return _mcp_error(f"Failed to start {base_tool.name}: {e}")
-            # No callback — fall through to synchronous execution
-            logger.warning(
-                f"[SDK] No long-running callback for {base_tool.name}, "
-                f"executing synchronously (may block)"
-            )
-
-        # --- Normal (fast) tool: execute synchronously ---
         try:
             return await _execute_tool_sync(base_tool, user_id, session, args)
         except Exception as e:

autogpt_platform/backend/backend/copilot/service_test.py

@@ -6,12 +6,7 @@ import pytest
 
 from . import service as chat_service
 from .model import create_chat_session, get_chat_session, upsert_chat_session
-from .response_model import (
-    StreamError,
-    StreamFinish,
-    StreamTextDelta,
-    StreamToolOutputAvailable,
-)
+from .response_model import StreamError, StreamTextDelta, StreamToolOutputAvailable
 from .sdk import service as sdk_service
 from .sdk.transcript import download_transcript
 
@@ -30,7 +25,6 @@ async def test_stream_chat_completion(setup_test_user, test_user_id):
     session = await create_chat_session(test_user_id)
 
     has_errors = False
-    has_ended = False
     assistant_message = ""
     async for chunk in chat_service.stream_chat_completion(
         session.session_id, "Hello, how are you?", user_id=session.user_id
@@ -40,10 +34,9 @@ async def test_stream_chat_completion(setup_test_user, test_user_id):
             has_errors = True
         if isinstance(chunk, StreamTextDelta):
             assistant_message += chunk.delta
-        if isinstance(chunk, StreamFinish):
-            has_ended = True
 
-    assert has_ended, "Chat completion did not end"
+    # StreamFinish is published by mark_session_completed (processor layer),
+    # not by the service. The generator completing means the stream ended.
     assert not has_errors, "Error occurred while streaming chat completion"
     assert assistant_message, "Assistant message is empty"
 
@@ -61,7 +54,6 @@ async def test_stream_chat_completion_with_tool_calls(setup_test_user, test_user
     session = await upsert_chat_session(session)
 
     has_errors = False
-    has_ended = False
     had_tool_calls = False
     async for chunk in chat_service.stream_chat_completion(
         session.session_id,
@@ -71,13 +63,9 @@ async def test_stream_chat_completion_with_tool_calls(setup_test_user, test_user
         logger.info(chunk)
         if isinstance(chunk, StreamError):
             has_errors = True
-
-        if isinstance(chunk, StreamFinish):
-            has_ended = True
         if isinstance(chunk, StreamToolOutputAvailable):
             had_tool_calls = True
 
-    assert has_ended, "Chat completion did not end"
     assert not has_errors, "Error occurred while streaming chat completion"
     assert had_tool_calls, "Tool calls did not occur"
     session = await get_chat_session(session.session_id)
@@ -114,7 +102,6 @@ async def test_sdk_resume_multi_turn(setup_test_user, test_user_id):
     )
     turn1_text = ""
     turn1_errors: list[str] = []
-    turn1_ended = False
 
     async for chunk in sdk_service.stream_chat_completion_sdk(
         session.session_id,
@@ -125,10 +112,7 @@ async def test_sdk_resume_multi_turn(setup_test_user, test_user_id):
             turn1_text += chunk.delta
         elif isinstance(chunk, StreamError):
             turn1_errors.append(chunk.errorText)
-        elif isinstance(chunk, StreamFinish):
-            turn1_ended = True
 
-    assert turn1_ended, "Turn 1 did not finish"
     assert not turn1_errors, f"Turn 1 errors: {turn1_errors}"
     assert turn1_text, "Turn 1 produced no text"
 
@@ -159,7 +143,6 @@ async def test_sdk_resume_multi_turn(setup_test_user, test_user_id):
     turn2_msg = "What was the special keyword I asked you to remember?"
     turn2_text = ""
     turn2_errors: list[str] = []
-    turn2_ended = False
 
     async for chunk in sdk_service.stream_chat_completion_sdk(
         session.session_id,
@@ -171,10 +154,7 @@ async def test_sdk_resume_multi_turn(setup_test_user, test_user_id):
             turn2_text += chunk.delta
         elif isinstance(chunk, StreamError):
             turn2_errors.append(chunk.errorText)
-        elif isinstance(chunk, StreamFinish):
-            turn2_ended = True
 
-    assert turn2_ended, "Turn 2 did not finish"
     assert not turn2_errors, f"Turn 2 errors: {turn2_errors}"
     assert turn2_text, "Turn 2 produced no text"
     assert keyword in turn2_text, (

autogpt_platform/backend/backend/copilot/test_copilot_e2e.py

@@ -0,0 +1,401 @@
+"""End-to-end tests for Copilot streaming with dummy implementations.
+
+These tests verify the complete copilot flow using dummy implementations
+for agent generator and SDK service, allowing automated testing without
+external LLM calls.
+
+Enable test mode with COPILOT_TEST_MODE=true environment variable.
+
+Note: StreamFinish is NOT emitted by the dummy service — it is published
+by mark_session_completed in the processor layer.  These tests only cover
+the service-level streaming output (StreamStart + StreamTextDelta).
+"""
+
+import asyncio
+import os
+from uuid import uuid4
+
+import pytest
+
+from backend.copilot.model import ChatMessage, ChatSession, upsert_chat_session
+from backend.copilot.response_model import (
+    StreamError,
+    StreamHeartbeat,
+    StreamStart,
+    StreamTextDelta,
+)
+from backend.copilot.sdk.dummy import stream_chat_completion_dummy
+
+
+@pytest.fixture(autouse=True)
+def enable_test_mode():
+    """Enable test mode for all tests in this module."""
+    os.environ["COPILOT_TEST_MODE"] = "true"
+    yield
+    os.environ.pop("COPILOT_TEST_MODE", None)
+
+
+@pytest.mark.asyncio
+async def test_dummy_streaming_basic_flow():
+    """Test that dummy streaming produces correct event sequence."""
+    events = []
+
+    async for event in stream_chat_completion_dummy(
+        session_id="test-session-basic",
+        message="Hello",
+        is_user_message=True,
+        user_id="test-user",
+    ):
+        events.append(event)
+
+    # Verify we got events
+    assert len(events) > 0, "Should receive events"
+
+    # Verify StreamStart
+    start_events = [e for e in events if isinstance(e, StreamStart)]
+    assert len(start_events) == 1
+    assert start_events[0].messageId
+    assert start_events[0].sessionId
+
+    # Verify StreamTextDelta events
+    text_events = [e for e in events if isinstance(e, StreamTextDelta)]
+    assert len(text_events) > 0
+    full_text = "".join(e.delta for e in text_events)
+    assert len(full_text) > 0
+
+    # Verify order: start before text
+    start_idx = events.index(start_events[0])
+    first_text_idx = events.index(text_events[0]) if text_events else -1
+    if first_text_idx >= 0:
+        assert start_idx < first_text_idx
+
+    print(f"✅ Basic flow: {len(events)} events, {len(text_events)} text deltas")
+
+
+@pytest.mark.asyncio
+async def test_streaming_no_timeout():
+    """Test that streaming completes within reasonable time without timeout."""
+    import time
+
+    start_time = time.monotonic()
+    event_count = 0
+
+    async for _event in stream_chat_completion_dummy(
+        session_id="test-session-timeout",
+        message="count to 10",
+        is_user_message=True,
+        user_id="test-user",
+    ):
+        event_count += 1
+
+    elapsed = time.monotonic() - start_time
+
+    # Should complete in < 5 seconds (dummy has 0.1s delays between words)
+    assert elapsed < 5.0, f"Streaming took {elapsed:.1f}s, expected < 5s"
+    assert event_count > 0, "Should receive events"
+
+    print(f"✅ No timeout: completed in {elapsed:.2f}s with {event_count} events")
+
+
+@pytest.mark.asyncio
+async def test_streaming_event_types():
+    """Test that all expected event types are present."""
+    event_types = set()
+
+    async for event in stream_chat_completion_dummy(
+        session_id="test-session-types",
+        message="test",
+        is_user_message=True,
+        user_id="test-user",
+    ):
+        event_types.add(type(event).__name__)
+
+    # Required event types (StreamFinish is published by processor, not service)
+    assert "StreamStart" in event_types, "Missing StreamStart"
+    assert "StreamTextDelta" in event_types, "Missing StreamTextDelta"
+
+    print(f"✅ Event types: {sorted(event_types)}")
+
+
+@pytest.mark.asyncio
+async def test_streaming_text_content():
+    """Test that streamed text is coherent and complete."""
+    text_events = []
+
+    async for event in stream_chat_completion_dummy(
+        session_id="test-session-content",
+        message="count to 3",
+        is_user_message=True,
+        user_id="test-user",
+    ):
+        if isinstance(event, StreamTextDelta):
+            text_events.append(event)
+
+    # Verify text deltas
+    assert len(text_events) > 0, "Should have text deltas"
+
+    # Reconstruct full text
+    full_text = "".join(e.delta for e in text_events)
+    assert len(full_text) > 0, "Text should not be empty"
+    assert (
+        "1" in full_text or "counted" in full_text.lower()
+    ), "Text should contain count"
+
+    # Verify all deltas have IDs
+    for text_event in text_events:
+        assert text_event.id, "Text delta must have ID"
+        assert text_event.delta, "Text delta must have content"
+
+    print(f"✅ Text content: '{full_text}' ({len(text_events)} deltas)")
+
+
+@pytest.mark.asyncio
+async def test_streaming_heartbeat_timing():
+    """Test that heartbeats are sent at correct interval during long operations."""
+    # This test would need a dummy that takes longer
+    # For now, just verify heartbeat structure if we receive one
+    heartbeats = []
+
+    async for event in stream_chat_completion_dummy(
+        session_id="test-session-heartbeat",
+        message="test",
+        is_user_message=True,
+        user_id="test-user",
+    ):
+        if isinstance(event, StreamHeartbeat):
+            heartbeats.append(event)
+
+    # Dummy is fast, so we might not get heartbeats
+    # But if we do, verify they're valid
+    if heartbeats:
+        print(f"✅ Heartbeat structure verified ({len(heartbeats)} received)")
+    else:
+        print("✅ No heartbeats (dummy executes quickly)")
+
+
+@pytest.mark.asyncio
+async def test_error_handling():
+    """Test that errors are properly formatted and sent."""
+    # This would require a dummy that can trigger errors
+    # For now, just verify error event structure
+
+    error = StreamError(errorText="Test error", code="test_error")
+    assert error.errorText == "Test error"
+    assert error.code == "test_error"
+    assert str(error.type.value) in ["error", "error"]
+
+    print("✅ Error structure verified")
+
+
+@pytest.mark.asyncio
+async def test_concurrent_sessions():
+    """Test that multiple sessions can stream concurrently."""
+
+    async def stream_session(session_id: str) -> int:
+        count = 0
+        async for _event in stream_chat_completion_dummy(
+            session_id=session_id,
+            message="test",
+            is_user_message=True,
+            user_id="test-user",
+        ):
+            count += 1
+        return count
+
+    # Run 3 concurrent sessions
+    results = await asyncio.gather(
+        stream_session("session-1"),
+        stream_session("session-2"),
+        stream_session("session-3"),
+    )
+
+    # All should complete successfully
+    assert all(count > 0 for count in results), "All sessions should produce events"
+    print(f"✅ Concurrent sessions: {results} events each")
+
+
+@pytest.mark.asyncio
+@pytest.mark.xfail(
+    reason="Event loop isolation issue with DB operations in tests - needs fixture refactoring"
+)
+async def test_session_state_persistence():
+    """Test that session state is maintained across multiple messages."""
+    from datetime import datetime, timezone
+
+    session_id = f"test-session-{uuid4()}"
+    user_id = "test-user"
+
+    # Create session with first message
+    session = ChatSession(
+        session_id=session_id,
+        user_id=user_id,
+        messages=[
+            ChatMessage(role="user", content="Hello"),
+            ChatMessage(role="assistant", content="Hi there!"),
+        ],
+        usage=[],
+        started_at=datetime.now(timezone.utc),
+        updated_at=datetime.now(timezone.utc),
+    )
+    await upsert_chat_session(session)
+
+    # Stream second message
+    events = []
+    async for event in stream_chat_completion_dummy(
+        session_id=session_id,
+        message="How are you?",
+        is_user_message=True,
+        user_id=user_id,
+        session=session,  # Pass existing session
+    ):
+        events.append(event)
+
+    # Verify events were produced
+    assert len(events) > 0, "Should produce events for second message"
+
+    print(f"✅ Session persistence: {len(events)} events for second message")
+
+
+@pytest.mark.asyncio
+async def test_message_deduplication():
+    """Test that duplicate messages are filtered out."""
+
+    # Simulate receiving duplicate events (e.g., from reconnection)
+    events = []
+
+    # First stream
+    async for event in stream_chat_completion_dummy(
+        session_id="test-dedup-1",
+        message="Hello",
+        is_user_message=True,
+        user_id="test-user",
+    ):
+        events.append(event)
+
+    # Count unique message IDs in StreamStart events
+    start_events = [e for e in events if isinstance(e, StreamStart)]
+    message_ids = [e.messageId for e in start_events]
+
+    # Verify all IDs are present
+    assert len(message_ids) == len(set(message_ids)), "Message IDs should be unique"
+
+    print(f"✅ Deduplication: {len(events)} events, all unique")
+
+
+@pytest.mark.asyncio
+async def test_event_ordering():
+    """Test that events arrive in correct order."""
+    events = []
+
+    async for event in stream_chat_completion_dummy(
+        session_id="test-ordering",
+        message="Test",
+        is_user_message=True,
+        user_id="test-user",
+    ):
+        events.append(event)
+
+    # Find event indices
+    start_idx = next(
+        (i for i, e in enumerate(events) if isinstance(e, StreamStart)), None
+    )
+    text_indices = [i for i, e in enumerate(events) if isinstance(e, StreamTextDelta)]
+
+    # Verify ordering
+    assert start_idx is not None, "Should have StreamStart"
+    assert start_idx == 0, "StreamStart should be first"
+
+    if text_indices:
+        assert all(
+            start_idx < i for i in text_indices
+        ), "Text deltas should be after start"
+
+    print(f"✅ Event ordering: start({start_idx}) < text deltas")
+
+
+@pytest.mark.asyncio
+async def test_stream_completeness():
+    """Test that stream includes all required event types."""
+    events = []
+
+    async for event in stream_chat_completion_dummy(
+        session_id="test-completeness",
+        message="Complete stream test",
+        is_user_message=True,
+        user_id="test-user",
+    ):
+        events.append(event)
+
+    # Check for required events (StreamFinish is published by processor)
+    has_start = any(isinstance(e, StreamStart) for e in events)
+    has_text = any(isinstance(e, StreamTextDelta) for e in events)
+
+    assert has_start, "Stream must include StreamStart"
+    assert has_text, "Stream must include text deltas"
+
+    # Verify exactly one start
+    start_count = sum(1 for e in events if isinstance(e, StreamStart))
+    assert start_count == 1, f"Should have exactly 1 StreamStart, got {start_count}"
+
+    print(
+        f"✅ Completeness: 1 start, {sum(1 for e in events if isinstance(e, StreamTextDelta))} text deltas"
+    )
+
+
+@pytest.mark.asyncio
+async def test_text_delta_consistency():
+    """Test that text deltas have consistent IDs and build coherent text."""
+    text_events = []
+
+    async for event in stream_chat_completion_dummy(
+        session_id="test-consistency",
+        message="Test consistency",
+        is_user_message=True,
+        user_id="test-user",
+    ):
+        if isinstance(event, StreamTextDelta):
+            text_events.append(event)
+
+    # Verify all text deltas have IDs
+    assert all(e.id for e in text_events), "All text deltas must have IDs"
+
+    # Verify all deltas have the same ID (same text block)
+    if text_events:
+        first_id = text_events[0].id
+        assert all(
+            e.id == first_id for e in text_events
+        ), "All text deltas should share the same block ID"
+
+    # Verify deltas build coherent text
+    full_text = "".join(e.delta for e in text_events)
+    assert len(full_text) > 0, "Deltas should build non-empty text"
+    assert (
+        full_text == full_text.strip()
+    ), "Text should not have leading/trailing whitespace artifacts"
+
+    print(
+        f"✅ Consistency: {len(text_events)} deltas with ID '{text_events[0].id if text_events else 'N/A'}', text: '{full_text}'"
+    )
+
+
+if __name__ == "__main__":
+    # Run tests directly
+
+    print("Running Copilot E2E tests with dummy implementations...")
+    print("=" * 60)
+
+    asyncio.run(test_dummy_streaming_basic_flow())
+    asyncio.run(test_streaming_no_timeout())
+    asyncio.run(test_streaming_event_types())
+    asyncio.run(test_streaming_text_content())
+    asyncio.run(test_streaming_heartbeat_timing())
+    asyncio.run(test_error_handling())
+    asyncio.run(test_concurrent_sessions())
+    asyncio.run(test_session_state_persistence())
+    asyncio.run(test_message_deduplication())
+    asyncio.run(test_event_ordering())
+    asyncio.run(test_stream_completeness())
+    asyncio.run(test_text_delta_consistency())
+
+    print("=" * 60)
+    print("✅ All E2E tests passed!")

autogpt_platform/backend/backend/copilot/tools/__init__.py

@@ -10,7 +10,6 @@ from .add_understanding import AddUnderstandingTool
 from .agent_output import AgentOutputTool
 from .base import BaseTool
 from .bash_exec import BashExecTool
-from .check_operation_status import CheckOperationStatusTool
 from .create_agent import CreateAgentTool
 from .customize_agent import CustomizeAgentTool
 from .edit_agent import EditAgentTool
@@ -47,7 +46,6 @@ TOOL_REGISTRY: dict[str, BaseTool] = {
     "run_agent": RunAgentTool(),
     "run_block": RunBlockTool(),
     "view_agent_output": AgentOutputTool(),
-    "check_operation_status": CheckOperationStatusTool(),
     "search_docs": SearchDocsTool(),
     "get_doc_page": GetDocPageTool(),
     # Web fetch for safe URL retrieval

autogpt_platform/backend/backend/copilot/tools/_test_data.py

@@ -3,6 +3,7 @@ from datetime import UTC, datetime
 from os import getenv
 
 import pytest
+import pytest_asyncio
 from prisma.types import ProfileCreateInput
 from pydantic import SecretStr
 
@@ -31,14 +32,16 @@ def make_session(user_id: str):
     )
 
 
-@pytest.fixture(scope="session")
-async def setup_test_data():
+@pytest_asyncio.fixture(scope="session", loop_scope="session")
+async def setup_test_data(server):
     """
     Set up test data for run_agent tests:
     1. Create a test user
     2. Create a test graph (agent input -> agent output)
     3. Create a store listing and store listing version
     4. Approve the store listing version
+
+    Depends on ``server`` to ensure Prisma is connected.
     """
     # 1. Create a test user
     user_data = {
@@ -150,14 +153,16 @@ async def setup_test_data():
     }
 
 
-@pytest.fixture(scope="session")
-async def setup_llm_test_data():
+@pytest_asyncio.fixture(scope="session", loop_scope="session")
+async def setup_llm_test_data(server):
     """
     Set up test data for LLM agent tests:
     1. Create a test user
     2. Create test OpenAI credentials for the user
     3. Create a test graph with input -> LLM block -> output
     4. Create and approve a store listing
+
+    Depends on ``server`` to ensure Prisma is connected.
     """
     key = getenv("OPENAI_API_KEY")
     if not key:
@@ -315,13 +320,15 @@ async def setup_llm_test_data():
     }
 
 
-@pytest.fixture(scope="session")
-async def setup_firecrawl_test_data():
+@pytest_asyncio.fixture(scope="session", loop_scope="session")
+async def setup_firecrawl_test_data(server):
     """
     Set up test data for Firecrawl agent tests (missing credentials scenario):
     1. Create a test user (WITHOUT Firecrawl credentials)
     2. Create a test graph with input -> Firecrawl block -> output
     3. Create and approve a store listing
+
+    Depends on ``server`` to ensure Prisma is connected.
     """
     # 1. Create a test user
     user_data = {

autogpt_platform/backend/backend/copilot/tools/agent_generator/__init__.py

@@ -19,6 +19,7 @@ from .core import (
     get_all_relevant_agents_for_generation,
     get_library_agent_by_graph_id,
     get_library_agent_by_id,
+    get_library_agents_by_ids,
     get_library_agents_for_generation,
     graph_to_json,
     json_to_graph,
@@ -49,6 +50,7 @@ __all__ = [
     "get_all_relevant_agents_for_generation",
     "get_library_agent_by_graph_id",
     "get_library_agent_by_id",
+    "get_library_agents_by_ids",
     "get_library_agents_for_generation",
     "get_user_message_for_error",
     "graph_to_json",

autogpt_platform/backend/backend/copilot/tools/agent_generator/core.py

@@ -3,6 +3,7 @@
 import logging
 import re
 import uuid
+from collections.abc import Sequence
 from typing import Any, NotRequired, TypedDict
 
 from backend.data.db_accessors import graph_db, library_db, store_db
@@ -78,7 +79,7 @@ AgentSummary = LibraryAgentSummary | MarketplaceAgentSummary | dict[str, Any]
 
 
 def _to_dict_list(
-    agents: list[AgentSummary] | list[dict[str, Any]] | None,
+    agents: Sequence[AgentSummary] | Sequence[dict[str, Any]] | None,
 ) -> list[dict[str, Any]] | None:
     """Convert typed agent summaries to plain dicts for external service calls."""
     if agents is None:
@@ -190,6 +191,36 @@ async def get_library_agent_by_id(
 get_library_agent_by_graph_id = get_library_agent_by_id
 
 
+async def get_library_agents_by_ids(
+    user_id: str,
+    agent_ids: list[str],
+) -> list[LibraryAgentSummary]:
+    """Fetch multiple library agents by their IDs.
+
+    Args:
+        user_id: The user ID
+        agent_ids: List of agent IDs (can be graph_ids or library agent IDs)
+
+    Returns:
+        List of LibraryAgentSummary for found agents (silently skips not found)
+    """
+    agents: list[LibraryAgentSummary] = []
+    for agent_id in agent_ids:
+        try:
+            agent = await get_library_agent_by_id(user_id, agent_id)
+            if agent:
+                agents.append(agent)
+                logger.debug(f"Fetched library agent by ID: {agent['name']}")
+            else:
+                logger.warning(f"Library agent not found for ID: {agent_id}")
+        except Exception as e:
+            logger.warning(f"Failed to fetch library agent {agent_id}: {e}")
+            continue
+
+    logger.info(f"Fetched {len(agents)}/{len(agent_ids)} library agents by ID")
+    return agents
+
+
 async def get_library_agents_for_generation(
     user_id: str,
     search_query: str | None = None,
@@ -214,10 +245,17 @@ async def get_library_agents_for_generation(
     Returns:
         List of LibraryAgentSummary with schemas and recent executions for sub-agent composition
     """
+    search_term = search_query.strip() if search_query else None
+    if search_term and len(search_term) > 100:
+        raise ValueError(
+            f"Search query is too long ({len(search_term)} chars, max 100). "
+            f"Please use a shorter, more specific search term."
+        )
+
     try:
         response = await library_db().list_library_agents(
             user_id=user_id,
-            search_term=search_query,
+            search_term=search_term,
             page=1,
             page_size=max_results,
             include_executions=True,
@@ -271,9 +309,16 @@ async def search_marketplace_agents_for_generation(
     Returns:
         List of LibraryAgentSummary with full input/output schemas
     """
+    search_term = search_query.strip()
+    if len(search_term) > 100:
+        raise ValueError(
+            f"Search query is too long ({len(search_term)} chars, max 100). "
+            f"Please use a shorter, more specific search term."
+        )
+
     try:
         response = await store_db().get_store_agents(
-            search_query=search_query,
+            search_query=search_term,
             page=1,
             page_size=max_results,
         )
@@ -424,7 +469,7 @@ def extract_search_terms_from_steps(
 async def enrich_library_agents_from_steps(
     user_id: str,
     decomposition_result: DecompositionResult | dict[str, Any],
-    existing_agents: list[AgentSummary] | list[dict[str, Any]],
+    existing_agents: Sequence[AgentSummary] | Sequence[dict[str, Any]],
     exclude_graph_id: str | None = None,
     include_marketplace: bool = True,
     max_additional_results: int = 10,
@@ -448,7 +493,7 @@ async def enrich_library_agents_from_steps(
     search_terms = extract_search_terms_from_steps(decomposition_result)
 
     if not search_terms:
-        return existing_agents
+        return list(existing_agents)
 
     existing_ids: set[str] = set()
     existing_names: set[str] = set()
@@ -511,7 +556,7 @@ async def enrich_library_agents_from_steps(
 async def decompose_goal(
     description: str,
     context: str = "",
-    library_agents: list[AgentSummary] | None = None,
+    library_agents: Sequence[AgentSummary] | None = None,
 ) -> DecompositionResult | None:
     """Break down a goal into steps or return clarifying questions.
 
@@ -539,22 +584,16 @@ async def decompose_goal(
 
 async def generate_agent(
     instructions: DecompositionResult | dict[str, Any],
-    library_agents: list[AgentSummary] | list[dict[str, Any]] | None = None,
-    operation_id: str | None = None,
-    task_id: str | None = None,
+    library_agents: Sequence[AgentSummary] | Sequence[dict[str, Any]] | None = None,
 ) -> dict[str, Any] | None:
     """Generate agent JSON from instructions.
 
     Args:
         instructions: Structured instructions from decompose_goal
         library_agents: User's library agents available for sub-agent composition
-        operation_id: Operation ID for async processing (enables Redis Streams
-            completion notification)
-        task_id: Task ID for async processing (enables Redis Streams persistence
-            and SSE delivery)
 
     Returns:
-        Agent JSON dict, {"status": "accepted"} for async, error dict {"type": "error", ...}, or None on error
+        Agent JSON dict, error dict {"type": "error", ...}, or None on error
 
     Raises:
         AgentGeneratorNotConfiguredError: If the external service is not configured.
@@ -562,13 +601,9 @@ async def generate_agent(
     _check_service_configured()
     logger.info("Calling external Agent Generator service for generate_agent")
     result = await generate_agent_external(
-        dict(instructions), _to_dict_list(library_agents), operation_id, task_id
+        dict(instructions), _to_dict_list(library_agents)
     )
 
-    # Don't modify async response
-    if result and result.get("status") == "accepted":
-        return result
-
     if result:
         if isinstance(result, dict) and result.get("type") == "error":
             return result
@@ -758,9 +793,7 @@ async def get_agent_as_json(
 async def generate_agent_patch(
     update_request: str,
     current_agent: dict[str, Any],
-    library_agents: list[AgentSummary] | None = None,
-    operation_id: str | None = None,
-    task_id: str | None = None,
+    library_agents: Sequence[AgentSummary] | None = None,
 ) -> dict[str, Any] | None:
     """Update an existing agent using natural language.
 
@@ -773,12 +806,10 @@ async def generate_agent_patch(
         update_request: Natural language description of changes
         current_agent: Current agent JSON
         library_agents: User's library agents available for sub-agent composition
-        operation_id: Operation ID for async processing (enables Redis Streams callback)
-        task_id: Task ID for async processing (enables Redis Streams callback)
 
     Returns:
         Updated agent JSON, clarifying questions dict {"type": "clarifying_questions", ...},
-        {"status": "accepted"} for async, error dict {"type": "error", ...}, or None on error
+        error dict {"type": "error", ...}, or None on error
 
     Raises:
         AgentGeneratorNotConfiguredError: If the external service is not configured.
@@ -789,8 +820,6 @@ async def generate_agent_patch(
         update_request,
         current_agent,
         _to_dict_list(library_agents),
-        operation_id,
-        task_id,
     )
 
 

autogpt_platform/backend/backend/copilot/tools/agent_generator/dummy.py

@@ -102,10 +102,15 @@ async def generate_agent_dummy(
     instructions: dict[str, Any],
     library_agents: list[dict[str, Any]] | None = None,
     operation_id: str | None = None,
-    task_id: str | None = None,
+    session_id: str | None = None,
 ) -> dict[str, Any]:
-    """Return dummy agent JSON after a simulated delay."""
-    logger.info("Using dummy agent generator for generate_agent (30s delay)")
+    """Return dummy agent synchronously (blocks for 30s, returns agent JSON).
+
+    Note: operation_id and session_id parameters are ignored - we always use synchronous mode.
+    """
+    logger.info(
+        "Using dummy agent generator (sync mode): returning agent JSON after 30s"
+    )
     await asyncio.sleep(30)
     return _generate_dummy_agent_json()
 
@@ -115,10 +120,16 @@ async def generate_agent_patch_dummy(
     current_agent: dict[str, Any],
     library_agents: list[dict[str, Any]] | None = None,
     operation_id: str | None = None,
-    task_id: str | None = None,
+    session_id: str | None = None,
 ) -> dict[str, Any]:
-    """Return dummy patched agent (returns the current agent with updated description)."""
-    logger.info("Using dummy agent generator for generate_agent_patch")
+    """Return dummy patched agent synchronously (blocks for 30s, returns patched agent JSON).
+
+    Note: operation_id and session_id parameters are ignored - we always use synchronous mode.
+    """
+    logger.info(
+        "Using dummy agent generator patch (sync mode): returning patched agent after 30s"
+    )
+    await asyncio.sleep(30)
     patched = current_agent.copy()
     patched["description"] = (
         f"{current_agent.get('description', '')} (updated: {update_request})"

autogpt_platform/backend/backend/copilot/tools/agent_generator/service.py

@@ -1,11 +1,13 @@
 """External Agent Generator service client.
 
 This module provides a client for communicating with the external Agent Generator
-microservice. When AGENTGENERATOR_HOST is configured, the agent generation functions
-will delegate to the external service instead of using the built-in LLM-based implementation.
+microservice. All generation endpoints use async polling: submit a job (202),
+then poll GET /api/jobs/{job_id} every few seconds until the result is ready.
 """
 
+import asyncio
 import logging
+import time
 from typing import Any
 
 import httpx
@@ -25,22 +27,21 @@ logger = logging.getLogger(__name__)
 
 _dummy_mode_warned = False
 
+# ---------------------------------------------------------------------------
+# Shared helpers
+# ---------------------------------------------------------------------------
+
+POLL_INTERVAL_SECONDS = 10.0
+MAX_POLL_TIME_SECONDS = 1800.0  # 30 minutes
+MAX_CONSECUTIVE_POLL_ERRORS = 5
+
 
 def _create_error_response(
     error_message: str,
     error_type: str = "unknown",
     details: dict[str, Any] | None = None,
 ) -> dict[str, Any]:
-    """Create a standardized error response dict.
-
-    Args:
-        error_message: Human-readable error message
-        error_type: Machine-readable error type
-        details: Optional additional error details
-
-    Returns:
-        Error dict with type="error" and error details
-    """
+    """Create a standardized error response dict."""
     response: dict[str, Any] = {
         "type": "error",
         "error": error_message,
@@ -52,14 +53,7 @@ def _create_error_response(
 
 
 def _classify_http_error(e: httpx.HTTPStatusError) -> tuple[str, str]:
-    """Classify an HTTP error into error_type and message.
-
-    Args:
-        e: The HTTP status error
-
-    Returns:
-        Tuple of (error_type, error_message)
-    """
+    """Classify an HTTP error into error_type and message."""
     status = e.response.status_code
     if status == 429:
         return "rate_limit", f"Agent Generator rate limited: {e}"
@@ -72,14 +66,7 @@ def _classify_http_error(e: httpx.HTTPStatusError) -> tuple[str, str]:
 
 
 def _classify_request_error(e: httpx.RequestError) -> tuple[str, str]:
-    """Classify a request error into error_type and message.
-
-    Args:
-        e: The request error
-
-    Returns:
-        Tuple of (error_type, error_message)
-    """
+    """Classify a request error into error_type and message."""
     error_str = str(e).lower()
     if "timeout" in error_str or "timed out" in error_str:
         return "timeout", f"Agent Generator request timed out: {e}"
@@ -89,6 +76,10 @@ def _classify_request_error(e: httpx.RequestError) -> tuple[str, str]:
         return "request_error", f"Request error calling Agent Generator: {e}"
 
 
+# ---------------------------------------------------------------------------
+# Client / settings singletons
+# ---------------------------------------------------------------------------
+
 _client: httpx.AsyncClient | None = None
 _settings: Settings | None = None
 
@@ -136,13 +127,149 @@ def _get_client() -> httpx.AsyncClient:
     global _client
     if _client is None:
         settings = _get_settings()
+        timeout = httpx.Timeout(float(settings.config.agentgenerator_timeout))
         _client = httpx.AsyncClient(
             base_url=_get_base_url(),
-            timeout=httpx.Timeout(settings.config.agentgenerator_timeout),
+            timeout=timeout,
         )
     return _client
 
 
+# ---------------------------------------------------------------------------
+# Core polling helper
+# ---------------------------------------------------------------------------
+
+
+async def _submit_and_poll(
+    endpoint: str,
+    payload: dict[str, Any],
+) -> dict[str, Any]:
+    """Submit a job to the agent-generator and poll until the result is ready.
+
+    The endpoint is expected to return 202 with ``{"job_id": "..."}`` on success.
+    We then poll ``GET /api/jobs/{job_id}`` every ``POLL_INTERVAL_SECONDS``
+    until the job completes or fails.
+
+    Returns:
+        The *result* dict from a completed job, or an error dict.
+    """
+    client = _get_client()
+
+    # 1. Submit ----------------------------------------------------------------
+    try:
+        response = await client.post(endpoint, json=payload)
+        response.raise_for_status()
+    except httpx.HTTPStatusError as e:
+        error_type, error_msg = _classify_http_error(e)
+        logger.error(error_msg)
+        return _create_error_response(error_msg, error_type)
+    except httpx.RequestError as e:
+        error_type, error_msg = _classify_request_error(e)
+        logger.error(error_msg)
+        return _create_error_response(error_msg, error_type)
+
+    data = response.json()
+    job_id = data.get("job_id")
+    if not job_id:
+        return _create_error_response(
+            "Agent Generator did not return a job_id", "invalid_response"
+        )
+
+    logger.info(f"Agent Generator job submitted: {job_id} via {endpoint}")
+
+    # 2. Poll ------------------------------------------------------------------
+    start = time.monotonic()
+    consecutive_errors = 0
+    while (time.monotonic() - start) < MAX_POLL_TIME_SECONDS:
+        await asyncio.sleep(POLL_INTERVAL_SECONDS)
+
+        try:
+            poll_resp = await client.get(f"/api/jobs/{job_id}")
+            poll_resp.raise_for_status()
+        except httpx.HTTPStatusError as e:
+            if e.response.status_code == 404:
+                return _create_error_response(
+                    "Agent Generator job not found or expired", "job_not_found"
+                )
+            status_code = e.response.status_code
+            if status_code in {429, 503, 504, 408}:
+                consecutive_errors += 1
+                logger.warning(
+                    f"Transient HTTP {status_code} polling job {job_id} "
+                    f"({consecutive_errors}/{MAX_CONSECUTIVE_POLL_ERRORS}): {e}"
+                )
+                if consecutive_errors >= MAX_CONSECUTIVE_POLL_ERRORS:
+                    error_type, error_msg = _classify_http_error(e)
+                    logger.error(
+                        f"Giving up on job {job_id} after "
+                        f"{MAX_CONSECUTIVE_POLL_ERRORS} consecutive poll errors: {error_msg}"
+                    )
+                    return _create_error_response(error_msg, error_type)
+                continue
+            error_type, error_msg = _classify_http_error(e)
+            logger.error(f"Poll error for job {job_id}: {error_msg}")
+            return _create_error_response(error_msg, error_type)
+        except httpx.RequestError as e:
+            consecutive_errors += 1
+            logger.warning(
+                f"Transient poll error for job {job_id} "
+                f"({consecutive_errors}/{MAX_CONSECUTIVE_POLL_ERRORS}): {e}"
+            )
+            if consecutive_errors >= MAX_CONSECUTIVE_POLL_ERRORS:
+                error_msg = (
+                    f"Giving up on job {job_id} after "
+                    f"{MAX_CONSECUTIVE_POLL_ERRORS} consecutive poll errors: {e}"
+                )
+                logger.error(error_msg)
+                return _create_error_response(error_msg, "poll_error")
+            continue
+
+        consecutive_errors = 0
+        poll_data = poll_resp.json()
+        status = poll_data.get("status")
+
+        if status == "completed":
+            logger.info(f"Agent Generator job {job_id} completed")
+            result = poll_data.get("result", {})
+            if not isinstance(result, dict):
+                return _create_error_response(
+                    "Agent Generator returned invalid result payload",
+                    "invalid_response",
+                )
+            return result
+        elif status == "failed":
+            error_msg = poll_data.get("error", "Job failed")
+            logger.error(f"Agent Generator job {job_id} failed: {error_msg}")
+            return _create_error_response(error_msg, "job_failed")
+        elif status in {"running", "pending", "queued"}:
+            continue
+        else:
+            return _create_error_response(
+                f"Agent Generator returned unexpected job status: {status}",
+                "invalid_response",
+            )
+
+    return _create_error_response("Agent generation timed out after polling", "timeout")
+
+
+def _extract_agent_json(result: dict[str, Any]) -> dict[str, Any]:
+    """Extract and validate agent_json from a job result.
+
+    Returns the agent_json dict, or an error response if missing/invalid.
+    """
+    agent_json = result.get("agent_json")
+    if not isinstance(agent_json, dict):
+        return _create_error_response(
+            "Agent Generator returned no agent_json in result", "invalid_response"
+        )
+    return agent_json
+
+
+# ---------------------------------------------------------------------------
+# Public functions — same signatures as before, now using polling
+# ---------------------------------------------------------------------------
+
+
 async def decompose_goal_external(
     description: str,
     context: str = "",
@@ -150,25 +277,17 @@ async def decompose_goal_external(
 ) -> dict[str, Any] | None:
     """Call the external service to decompose a goal.
 
-    Args:
-        description: Natural language goal description
-        context: Additional context (e.g., answers to previous questions)
-        library_agents: User's library agents available for sub-agent composition
+    Returns one of the following dicts (keyed by ``"type"``):
 
-    Returns:
-        Dict with either:
-        - {"type": "clarifying_questions", "questions": [...]}
-        - {"type": "instructions", "steps": [...]}
-        - {"type": "unachievable_goal", ...}
-        - {"type": "vague_goal", ...}
-        - {"type": "error", "error": "...", "error_type": "..."} on error
-        Or None on unexpected error
+    * ``{"type": "instructions", "steps": [...]}``
+    * ``{"type": "clarifying_questions", "questions": [...]}``
+    * ``{"type": "unachievable_goal", "reason": ..., "suggested_goal": ...}``
+    * ``{"type": "vague_goal", "suggested_goal": ...}``
+    * ``{"type": "error", "error": ..., "error_type": ...}``
     """
     if _is_dummy_mode():
         return await decompose_goal_dummy(description, context, library_agents)
 
-    client = _get_client()
-
     if context:
         description = f"{description}\n\nAdditional context from user:\n{context}"
 
@@ -177,236 +296,113 @@ async def decompose_goal_external(
         payload["library_agents"] = library_agents
 
     try:
-        response = await client.post("/api/decompose-description", json=payload)
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            error_msg = data.get("error", "Unknown error from Agent Generator")
-            error_type = data.get("error_type", "unknown")
-            logger.error(
-                f"Agent Generator decomposition failed: {error_msg} "
-                f"(type: {error_type})"
-            )
-            return _create_error_response(error_msg, error_type)
-
-        # Map the response to the expected format
-        response_type = data.get("type")
-        if response_type == "instructions":
-            return {"type": "instructions", "steps": data.get("steps", [])}
-        elif response_type == "clarifying_questions":
-            return {
-                "type": "clarifying_questions",
-                "questions": data.get("questions", []),
-            }
-        elif response_type == "unachievable_goal":
-            return {
-                "type": "unachievable_goal",
-                "reason": data.get("reason"),
-                "suggested_goal": data.get("suggested_goal"),
-            }
-        elif response_type == "vague_goal":
-            return {
-                "type": "vague_goal",
-                "suggested_goal": data.get("suggested_goal"),
-            }
-        elif response_type == "error":
-            # Pass through error from the service
-            return _create_error_response(
-                data.get("error", "Unknown error"),
-                data.get("error_type", "unknown"),
-            )
-        else:
-            logger.error(
-                f"Unknown response type from external service: {response_type}"
-            )
-            return _create_error_response(
-                f"Unknown response type from Agent Generator: {response_type}",
-                "invalid_response",
-            )
-
-    except httpx.HTTPStatusError as e:
-        error_type, error_msg = _classify_http_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
-    except httpx.RequestError as e:
-        error_type, error_msg = _classify_request_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
+        result = await _submit_and_poll("/api/decompose-description", payload)
     except Exception as e:
         error_msg = f"Unexpected error calling Agent Generator: {e}"
         logger.error(error_msg)
         return _create_error_response(error_msg, "unexpected_error")
 
+    # The result dict from the job is already in the expected format
+    # (type, steps, questions, etc.) — just return it as-is.
+    if result.get("type") == "error":
+        return result
+
+    response_type = result.get("type")
+    if response_type == "instructions":
+        return {"type": "instructions", "steps": result.get("steps", [])}
+    elif response_type == "clarifying_questions":
+        return {
+            "type": "clarifying_questions",
+            "questions": result.get("questions", []),
+        }
+    elif response_type == "unachievable_goal":
+        return {
+            "type": "unachievable_goal",
+            "reason": result.get("reason"),
+            "suggested_goal": result.get("suggested_goal"),
+        }
+    elif response_type == "vague_goal":
+        return {
+            "type": "vague_goal",
+            "suggested_goal": result.get("suggested_goal"),
+        }
+    else:
+        logger.error(f"Unknown response type from Agent Generator job: {response_type}")
+        return _create_error_response(
+            f"Unknown response type: {response_type}",
+            "invalid_response",
+        )
+
 
 async def generate_agent_external(
     instructions: dict[str, Any],
     library_agents: list[dict[str, Any]] | None = None,
-    operation_id: str | None = None,
-    task_id: str | None = None,
 ) -> dict[str, Any] | None:
     """Call the external service to generate an agent from instructions.
 
-    Args:
-        instructions: Structured instructions from decompose_goal
-        library_agents: User's library agents available for sub-agent composition
-        operation_id: Operation ID for async processing (enables Redis Streams callback)
-        task_id: Task ID for async processing (enables Redis Streams callback)
-
     Returns:
-        Agent JSON dict, {"status": "accepted"} for async, or error dict {"type": "error", ...} on error
+        Agent JSON dict or error dict {"type": "error", ...} on error.
     """
     if _is_dummy_mode():
-        return await generate_agent_dummy(
-            instructions, library_agents, operation_id, task_id
-        )
+        return await generate_agent_dummy(instructions, library_agents)
 
-    client = _get_client()
-
-    # Build request payload
     payload: dict[str, Any] = {"instructions": instructions}
     if library_agents:
         payload["library_agents"] = library_agents
-    if operation_id and task_id:
-        payload["operation_id"] = operation_id
-        payload["task_id"] = task_id
 
     try:
-        response = await client.post("/api/generate-agent", json=payload)
-
-        # Handle 202 Accepted for async processing
-        if response.status_code == 202:
-            logger.info(
-                f"Agent Generator accepted async request "
-                f"(operation_id={operation_id}, task_id={task_id})"
-            )
-            return {
-                "status": "accepted",
-                "operation_id": operation_id,
-                "task_id": task_id,
-            }
-
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            error_msg = data.get("error", "Unknown error from Agent Generator")
-            error_type = data.get("error_type", "unknown")
-            logger.error(
-                f"Agent Generator generation failed: {error_msg} (type: {error_type})"
-            )
-            return _create_error_response(error_msg, error_type)
-
-        return data.get("agent_json")
-
-    except httpx.HTTPStatusError as e:
-        error_type, error_msg = _classify_http_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
-    except httpx.RequestError as e:
-        error_type, error_msg = _classify_request_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
+        result = await _submit_and_poll("/api/generate-agent", payload)
     except Exception as e:
         error_msg = f"Unexpected error calling Agent Generator: {e}"
         logger.error(error_msg)
         return _create_error_response(error_msg, "unexpected_error")
 
+    if result.get("type") == "error":
+        return result
+
+    return _extract_agent_json(result)
+
 
 async def generate_agent_patch_external(
     update_request: str,
     current_agent: dict[str, Any],
     library_agents: list[dict[str, Any]] | None = None,
-    operation_id: str | None = None,
-    task_id: str | None = None,
 ) -> dict[str, Any] | None:
     """Call the external service to generate a patch for an existing agent.
 
-    Args:
-        update_request: Natural language description of changes
-        current_agent: Current agent JSON
-        library_agents: User's library agents available for sub-agent composition
-        operation_id: Operation ID for async processing (enables Redis Streams callback)
-        task_id: Task ID for async processing (enables Redis Streams callback)
-
     Returns:
-        Updated agent JSON, clarifying questions dict, {"status": "accepted"} for async, or error dict on error
+        Updated agent JSON, clarifying questions dict, or error dict.
     """
     if _is_dummy_mode():
         return await generate_agent_patch_dummy(
-            update_request, current_agent, library_agents, operation_id, task_id
+            update_request, current_agent, library_agents
         )
 
-    client = _get_client()
-
-    # Build request payload
     payload: dict[str, Any] = {
         "update_request": update_request,
         "current_agent_json": current_agent,
     }
     if library_agents:
         payload["library_agents"] = library_agents
-    if operation_id and task_id:
-        payload["operation_id"] = operation_id
-        payload["task_id"] = task_id
 
     try:
-        response = await client.post("/api/update-agent", json=payload)
-
-        # Handle 202 Accepted for async processing
-        if response.status_code == 202:
-            logger.info(
-                f"Agent Generator accepted async update request "
-                f"(operation_id={operation_id}, task_id={task_id})"
-            )
-            return {
-                "status": "accepted",
-                "operation_id": operation_id,
-                "task_id": task_id,
-            }
-
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            error_msg = data.get("error", "Unknown error from Agent Generator")
-            error_type = data.get("error_type", "unknown")
-            logger.error(
-                f"Agent Generator patch generation failed: {error_msg} "
-                f"(type: {error_type})"
-            )
-            return _create_error_response(error_msg, error_type)
-
-        # Check if it's clarifying questions
-        if data.get("type") == "clarifying_questions":
-            return {
-                "type": "clarifying_questions",
-                "questions": data.get("questions", []),
-            }
-
-        # Check if it's an error passed through
-        if data.get("type") == "error":
-            return _create_error_response(
-                data.get("error", "Unknown error"),
-                data.get("error_type", "unknown"),
-            )
-
-        # Otherwise return the updated agent JSON
-        return data.get("agent_json")
-
-    except httpx.HTTPStatusError as e:
-        error_type, error_msg = _classify_http_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
-    except httpx.RequestError as e:
-        error_type, error_msg = _classify_request_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
+        result = await _submit_and_poll("/api/update-agent", payload)
     except Exception as e:
         error_msg = f"Unexpected error calling Agent Generator: {e}"
         logger.error(error_msg)
         return _create_error_response(error_msg, "unexpected_error")
 
+    if result.get("type") == "error":
+        return result
+
+    if result.get("type") == "clarifying_questions":
+        return {
+            "type": "clarifying_questions",
+            "questions": result.get("questions", []),
+        }
+
+    return _extract_agent_json(result)
+
 
 async def customize_template_external(
     template_agent: dict[str, Any],
@@ -415,81 +411,51 @@ async def customize_template_external(
 ) -> dict[str, Any] | None:
     """Call the external service to customize a template/marketplace agent.
 
-    Args:
-        template_agent: The template agent JSON to customize
-        modification_request: Natural language description of customizations
-        context: Additional context (e.g., answers to previous questions)
-
     Returns:
-        Customized agent JSON, clarifying questions dict, or error dict on error
+        Customized agent JSON, clarifying questions dict, or error dict.
     """
     if _is_dummy_mode():
         return await customize_template_dummy(
             template_agent, modification_request, context
         )
 
-    client = _get_client()
-
-    request = modification_request
+    request_text = modification_request
     if context:
-        request = f"{modification_request}\n\nAdditional context from user:\n{context}"
+        request_text = (
+            f"{modification_request}\n\nAdditional context from user:\n{context}"
+        )
 
     payload: dict[str, Any] = {
         "template_agent_json": template_agent,
-        "modification_request": request,
+        "modification_request": request_text,
     }
 
     try:
-        response = await client.post("/api/template-modification", json=payload)
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            error_msg = data.get("error", "Unknown error from Agent Generator")
-            error_type = data.get("error_type", "unknown")
-            logger.error(
-                f"Agent Generator template customization failed: {error_msg} "
-                f"(type: {error_type})"
-            )
-            return _create_error_response(error_msg, error_type)
-
-        # Check if it's clarifying questions
-        if data.get("type") == "clarifying_questions":
-            return {
-                "type": "clarifying_questions",
-                "questions": data.get("questions", []),
-            }
-
-        # Check if it's an error passed through
-        if data.get("type") == "error":
-            return _create_error_response(
-                data.get("error", "Unknown error"),
-                data.get("error_type", "unknown"),
-            )
-
-        # Otherwise return the customized agent JSON
-        return data.get("agent_json")
-
-    except httpx.HTTPStatusError as e:
-        error_type, error_msg = _classify_http_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
-    except httpx.RequestError as e:
-        error_type, error_msg = _classify_request_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
+        result = await _submit_and_poll("/api/template-modification", payload)
     except Exception as e:
         error_msg = f"Unexpected error calling Agent Generator: {e}"
         logger.error(error_msg)
         return _create_error_response(error_msg, "unexpected_error")
 
+    if result.get("type") == "error":
+        return result
+
+    if result.get("type") == "clarifying_questions":
+        return {
+            "type": "clarifying_questions",
+            "questions": result.get("questions", []),
+        }
+
+    return _extract_agent_json(result)
+
+
+# ---------------------------------------------------------------------------
+# Non-generation endpoints (still synchronous — quick responses)
+# ---------------------------------------------------------------------------
+
 
 async def get_blocks_external() -> list[dict[str, Any]] | None:
-    """Get available blocks from the external service.
-
-    Returns:
-        List of block info dicts or None on error
-    """
+    """Get available blocks from the external service."""
     if _is_dummy_mode():
         return await get_blocks_dummy()
 
@@ -518,11 +484,7 @@ async def get_blocks_external() -> list[dict[str, Any]] | None:
 
 
 async def health_check() -> bool:
-    """Check if the external service is healthy.
-
-    Returns:
-        True if healthy, False otherwise
-    """
+    """Check if the external service is healthy."""
     if not is_external_service_configured():
         return False
 

autogpt_platform/backend/backend/copilot/tools/base.py

@@ -36,16 +36,6 @@ class BaseTool:
         """Whether this tool requires authentication."""
         return False
 
-    @property
-    def is_long_running(self) -> bool:
-        """Whether this tool is long-running and should execute in background.
-
-        Long-running tools (like agent generation) are executed via background
-        tasks to survive SSE disconnections. The result is persisted to chat
-        history and visible when the user refreshes.
-        """
-        return False
-
     def as_openai_tool(self) -> ChatCompletionToolParam:
         """Convert to OpenAI tool format."""
         return ChatCompletionToolParam(

autogpt_platform/backend/backend/copilot/tools/check_operation_status.py

@@ -1,124 +0,0 @@
-"""CheckOperationStatusTool — query the status of a long-running operation."""
-
-import logging
-from typing import Any
-
-from backend.copilot.model import ChatSession
-
-from .base import BaseTool
-from .models import ErrorResponse, ResponseType, ToolResponseBase
-
-logger = logging.getLogger(__name__)
-
-
-class OperationStatusResponse(ToolResponseBase):
-    """Response for check_operation_status tool."""
-
-    type: ResponseType = ResponseType.OPERATION_STATUS
-    task_id: str
-    operation_id: str
-    status: str  # "running", "completed", "failed"
-    tool_name: str | None = None
-    message: str = ""
-
-
-class CheckOperationStatusTool(BaseTool):
-    """Check the status of a long-running operation (create_agent, edit_agent, etc.).
-
-    The CoPilot uses this tool to report back to the user whether an
-    operation that was started earlier has completed, failed, or is still
-    running.
-    """
-
-    @property
-    def name(self) -> str:
-        return "check_operation_status"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Check the current status of a long-running operation such as "
-            "create_agent or edit_agent. Accepts either an operation_id or "
-            "task_id from a previous operation_started response. "
-            "Returns the current status: running, completed, or failed."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "operation_id": {
-                    "type": "string",
-                    "description": (
-                        "The operation_id from an operation_started response."
-                    ),
-                },
-                "task_id": {
-                    "type": "string",
-                    "description": (
-                        "The task_id from an operation_started response. "
-                        "Used as fallback if operation_id is not provided."
-                    ),
-                },
-            },
-            "required": [],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return False
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        from backend.copilot import stream_registry
-
-        operation_id = (kwargs.get("operation_id") or "").strip()
-        task_id = (kwargs.get("task_id") or "").strip()
-
-        if not operation_id and not task_id:
-            return ErrorResponse(
-                message="Please provide an operation_id or task_id.",
-                error="missing_parameter",
-            )
-
-        task = None
-        if operation_id:
-            task = await stream_registry.find_task_by_operation_id(operation_id)
-        if task is None and task_id:
-            task = await stream_registry.get_task(task_id)
-
-        if task is None:
-            # Task not in Redis — it may have already expired (TTL).
-            # Check conversation history for the result instead.
-            return ErrorResponse(
-                message=(
-                    "Operation not found — it may have already completed and "
-                    "expired from the status tracker. Check the conversation "
-                    "history for the result."
-                ),
-                error="not_found",
-            )
-
-        status_messages = {
-            "running": (
-                f"The {task.tool_name or 'operation'} is still running. "
-                "Please wait for it to complete."
-            ),
-            "completed": (
-                f"The {task.tool_name or 'operation'} has completed successfully."
-            ),
-            "failed": f"The {task.tool_name or 'operation'} has failed.",
-        }
-
-        return OperationStatusResponse(
-            task_id=task.task_id,
-            operation_id=task.operation_id,
-            status=task.status,
-            tool_name=task.tool_name,
-            message=status_messages.get(task.status, f"Status: {task.status}"),
-        )

autogpt_platform/backend/backend/copilot/tools/create_agent.py

@@ -10,7 +10,6 @@ from .agent_generator import (
     decompose_goal,
     enrich_library_agents_from_steps,
     generate_agent,
-    get_all_relevant_agents_for_generation,
     get_user_message_for_error,
     save_agent_to_library,
 )
@@ -18,7 +17,6 @@ from .base import BaseTool
 from .models import (
     AgentPreviewResponse,
     AgentSavedResponse,
-    AsyncProcessingResponse,
     ClarificationNeededResponse,
     ClarifyingQuestion,
     ErrorResponse,
@@ -40,17 +38,16 @@ class CreateAgentTool(BaseTool):
     def description(self) -> str:
         return (
             "Create a new agent workflow from a natural language description. "
-            "First generates a preview, then saves to library if save=true."
+            "First generates a preview, then saves to library if save=true. "
+            "\n\nIMPORTANT: Before calling this tool, search for relevant existing agents "
+            "using find_library_agent that could be used as building blocks. "
+            "Pass their IDs in the library_agent_ids parameter so the generator can compose them."
         )
 
     @property
     def requires_auth(self) -> bool:
         return True
 
-    @property
-    def is_long_running(self) -> bool:
-        return True
-
     @property
     def parameters(self) -> dict[str, Any]:
         return {
@@ -70,6 +67,15 @@ class CreateAgentTool(BaseTool):
                         "Include any preferences or constraints mentioned by the user."
                     ),
                 },
+                "library_agent_ids": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": (
+                        "List of library agent IDs to use as building blocks. "
+                        "Search for relevant agents using find_library_agent first, "
+                        "then pass their IDs here so they can be composed into the new agent."
+                    ),
+                },
                 "save": {
                     "type": "boolean",
                     "description": (
@@ -97,12 +103,14 @@ class CreateAgentTool(BaseTool):
         """
         description = kwargs.get("description", "").strip()
         context = kwargs.get("context", "")
+        library_agent_ids = kwargs.get("library_agent_ids", [])
         save = kwargs.get("save", True)
         session_id = session.session_id if session else None
 
-        # Extract async processing params (passed by long-running tool handler)
-        operation_id = kwargs.get("_operation_id")
-        task_id = kwargs.get("_task_id")
+        logger.info(
+            f"[AGENT_CREATE_DEBUG] START - description_len={len(description)}, "
+            f"library_agent_ids={library_agent_ids}, save={save}, user_id={user_id}, session_id={session_id}"
+        )
 
         if not description:
             return ErrorResponse(
@@ -111,25 +119,34 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
+        # Fetch library agents by IDs if provided
         library_agents = None
-        if user_id:
+        if user_id and library_agent_ids:
             try:
-                library_agents = await get_all_relevant_agents_for_generation(
+                from .agent_generator import get_library_agents_by_ids
+
+                library_agents = await get_library_agents_by_ids(
                     user_id=user_id,
-                    search_query=description,
-                    include_marketplace=True,
+                    agent_ids=library_agent_ids,
                 )
                 logger.debug(
-                    f"Found {len(library_agents)} relevant agents for sub-agent composition"
+                    f"Fetched {len(library_agents)} library agents by ID for sub-agent composition"
                 )
             except Exception as e:
-                logger.warning(f"Failed to fetch library agents: {e}")
+                logger.warning(f"Failed to fetch library agents by IDs: {e}")
 
         try:
             decomposition_result = await decompose_goal(
                 description, context, library_agents
             )
+            logger.info(
+                f"[AGENT_CREATE_DEBUG] DECOMPOSE - type={decomposition_result.get('type') if decomposition_result else None}, "
+                f"session_id={session_id}"
+            )
         except AgentGeneratorNotConfiguredError:
+            logger.error(
+                f"[AGENT_CREATE_DEBUG] ERROR - AgentGeneratorNotConfigured, session_id={session_id}"
+            )
             return ErrorResponse(
                 message=(
                     "Agent generation is not available. "
@@ -230,10 +247,17 @@ class CreateAgentTool(BaseTool):
             agent_json = await generate_agent(
                 decomposition_result,
                 library_agents,
-                operation_id=operation_id,
-                task_id=task_id,
+            )
+            logger.info(
+                f"[AGENT_CREATE_DEBUG] GENERATE - "
+                f"success={agent_json is not None}, "
+                f"is_error={isinstance(agent_json, dict) and agent_json.get('type') == 'error'}, "
+                f"session_id={session_id}"
             )
         except AgentGeneratorNotConfiguredError:
+            logger.error(
+                f"[AGENT_CREATE_DEBUG] ERROR - AgentGeneratorNotConfigured during generation, session_id={session_id}"
+            )
             return ErrorResponse(
                 message=(
                     "Agent generation is not available. "
@@ -276,25 +300,20 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if Agent Generator accepted for async processing
-        if agent_json.get("status") == "accepted":
-            logger.info(
-                f"Agent generation delegated to async processing "
-                f"(operation_id={operation_id}, task_id={task_id})"
-            )
-            return AsyncProcessingResponse(
-                message="Agent generation started. You'll be notified when it's complete.",
-                operation_id=operation_id,
-                task_id=task_id,
-                session_id=session_id,
-            )
-
         agent_name = agent_json.get("name", "Generated Agent")
         agent_description = agent_json.get("description", "")
         node_count = len(agent_json.get("nodes", []))
         link_count = len(agent_json.get("links", []))
 
+        logger.info(
+            f"[AGENT_CREATE_DEBUG] AGENT_JSON - name={agent_name}, "
+            f"nodes={node_count}, links={link_count}, save={save}, session_id={session_id}"
+        )
+
         if not save:
+            logger.info(
+                f"[AGENT_CREATE_DEBUG] RETURN - AgentPreviewResponse, session_id={session_id}"
+            )
             return AgentPreviewResponse(
                 message=(
                     f"I've generated an agent called '{agent_name}' with {node_count} blocks. "
@@ -320,6 +339,13 @@ class CreateAgentTool(BaseTool):
                 agent_json, user_id
             )
 
+            logger.info(
+                f"[AGENT_CREATE_DEBUG] SAVED - graph_id={created_graph.id}, "
+                f"library_agent_id={library_agent.id}, session_id={session_id}"
+            )
+            logger.info(
+                f"[AGENT_CREATE_DEBUG] RETURN - AgentSavedResponse, session_id={session_id}"
+            )
             return AgentSavedResponse(
                 message=f"Agent '{created_graph.name}' has been saved to your library!",
                 agent_id=created_graph.id,
@@ -330,6 +356,12 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
         except Exception as e:
+            logger.error(
+                f"[AGENT_CREATE_DEBUG] ERROR - save_failed: {str(e)}, session_id={session_id}"
+            )
+            logger.info(
+                f"[AGENT_CREATE_DEBUG] RETURN - ErrorResponse (save_failed), session_id={session_id}"
+            )
             return ErrorResponse(
                 message=f"Failed to save the agent: {str(e)}",
                 error="save_failed",

autogpt_platform/backend/backend/copilot/tools/create_agent_test.py

@@ -43,11 +43,6 @@ async def test_vague_goal_returns_suggested_goal_response(tool, session):
     }
 
     with (
-        patch(
-            "backend.copilot.tools.create_agent.get_all_relevant_agents_for_generation",
-            new_callable=AsyncMock,
-            return_value=[],
-        ),
         patch(
             "backend.copilot.tools.create_agent.decompose_goal",
             new_callable=AsyncMock,
@@ -78,11 +73,6 @@ async def test_unachievable_goal_returns_suggested_goal_response(tool, session):
     }
 
     with (
-        patch(
-            "backend.copilot.tools.create_agent.get_all_relevant_agents_for_generation",
-            new_callable=AsyncMock,
-            return_value=[],
-        ),
         patch(
             "backend.copilot.tools.create_agent.decompose_goal",
             new_callable=AsyncMock,
@@ -120,11 +110,6 @@ async def test_clarifying_questions_returns_clarification_needed_response(
     }
 
     with (
-        patch(
-            "backend.copilot.tools.create_agent.get_all_relevant_agents_for_generation",
-            new_callable=AsyncMock,
-            return_value=[],
-        ),
         patch(
             "backend.copilot.tools.create_agent.decompose_goal",
             new_callable=AsyncMock,

autogpt_platform/backend/backend/copilot/tools/customize_agent.py

@@ -46,10 +46,6 @@ class CustomizeAgentTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
-    @property
-    def is_long_running(self) -> bool:
-        return True
-
     @property
     def parameters(self) -> dict[str, Any]:
         return {

autogpt_platform/backend/backend/copilot/tools/edit_agent.py

@@ -9,7 +9,6 @@ from .agent_generator import (
     AgentGeneratorNotConfiguredError,
     generate_agent_patch,
     get_agent_as_json,
-    get_all_relevant_agents_for_generation,
     get_user_message_for_error,
     save_agent_to_library,
 )
@@ -17,7 +16,6 @@ from .base import BaseTool
 from .models import (
     AgentPreviewResponse,
     AgentSavedResponse,
-    AsyncProcessingResponse,
     ClarificationNeededResponse,
     ClarifyingQuestion,
     ErrorResponse,
@@ -38,17 +36,16 @@ class EditAgentTool(BaseTool):
     def description(self) -> str:
         return (
             "Edit an existing agent from the user's library using natural language. "
-            "Generates updates to the agent while preserving unchanged parts."
+            "Generates updates to the agent while preserving unchanged parts. "
+            "\n\nIMPORTANT: Before calling this tool, if the changes involve adding new "
+            "functionality, search for relevant existing agents using find_library_agent "
+            "that could be used as building blocks. Pass their IDs in library_agent_ids."
         )
 
     @property
     def requires_auth(self) -> bool:
         return True
 
-    @property
-    def is_long_running(self) -> bool:
-        return True
-
     @property
     def parameters(self) -> dict[str, Any]:
         return {
@@ -74,6 +71,15 @@ class EditAgentTool(BaseTool):
                         "Additional context or answers to previous clarifying questions."
                     ),
                 },
+                "library_agent_ids": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": (
+                        "List of library agent IDs to use as building blocks for the changes. "
+                        "If adding new functionality, search for relevant agents using "
+                        "find_library_agent first, then pass their IDs here."
+                    ),
+                },
                 "save": {
                     "type": "boolean",
                     "description": (
@@ -102,13 +108,10 @@ class EditAgentTool(BaseTool):
         agent_id = kwargs.get("agent_id", "").strip()
         changes = kwargs.get("changes", "").strip()
         context = kwargs.get("context", "")
+        library_agent_ids = kwargs.get("library_agent_ids", [])
         save = kwargs.get("save", True)
         session_id = session.session_id if session else None
 
-        # Extract async processing params (passed by long-running tool handler)
-        operation_id = kwargs.get("_operation_id")
-        task_id = kwargs.get("_task_id")
-
         if not agent_id:
             return ErrorResponse(
                 message="Please provide the agent ID to edit.",
@@ -132,21 +135,25 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
+        # Fetch library agents by IDs if provided
         library_agents = None
-        if user_id:
+        if user_id and library_agent_ids:
             try:
+                from .agent_generator import get_library_agents_by_ids
+
                 graph_id = current_agent.get("id")
-                library_agents = await get_all_relevant_agents_for_generation(
+                # Filter out the current agent being edited
+                filtered_ids = [id for id in library_agent_ids if id != graph_id]
+
+                library_agents = await get_library_agents_by_ids(
                     user_id=user_id,
-                    search_query=changes,
-                    exclude_graph_id=graph_id,
-                    include_marketplace=True,
+                    agent_ids=filtered_ids,
                 )
                 logger.debug(
-                    f"Found {len(library_agents)} relevant agents for sub-agent composition"
+                    f"Fetched {len(library_agents)} library agents by ID for sub-agent composition"
                 )
             except Exception as e:
-                logger.warning(f"Failed to fetch library agents: {e}")
+                logger.warning(f"Failed to fetch library agents by IDs: {e}")
 
         update_request = changes
         if context:
@@ -157,8 +164,6 @@ class EditAgentTool(BaseTool):
                 update_request,
                 current_agent,
                 library_agents,
-                operation_id=operation_id,
-                task_id=task_id,
             )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
@@ -178,19 +183,6 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if Agent Generator accepted for async processing
-        if result.get("status") == "accepted":
-            logger.info(
-                f"Agent edit delegated to async processing "
-                f"(operation_id={operation_id}, task_id={task_id})"
-            )
-            return AsyncProcessingResponse(
-                message="Agent edit started. You'll be notified when it's complete.",
-                operation_id=operation_id,
-                task_id=task_id,
-                session_id=session_id,
-            )
-
         # Check if the result is an error from the external service
         if isinstance(result, dict) and result.get("type") == "error":
             error_msg = result.get("error", "Unknown error")

autogpt_platform/backend/backend/copilot/tools/find_block_test.py

@@ -366,12 +366,15 @@ class TestFindBlockFiltering:
             return_value=(search_results, len(search_results))
         )
 
-        with patch(
-            "backend.copilot.tools.find_block.search",
-            return_value=mock_search_db,
-        ), patch(
-            "backend.copilot.tools.find_block.get_block",
-            side_effect=lambda bid: mock_blocks.get(bid),
+        with (
+            patch(
+                "backend.copilot.tools.find_block.search",
+                return_value=mock_search_db,
+            ),
+            patch(
+                "backend.copilot.tools.find_block.get_block",
+                side_effect=lambda bid: mock_blocks.get(bid),
+            ),
         ):
             tool = FindBlockTool()
             response = await tool._execute(

autogpt_platform/backend/backend/copilot/tools/models.py

@@ -36,8 +36,6 @@ class ResponseType(str, Enum):
     WORKSPACE_FILE_WRITTEN = "workspace_file_written"
     WORKSPACE_FILE_DELETED = "workspace_file_deleted"
     # Long-running operation types
-    OPERATION_STARTED = "operation_started"
-    OPERATION_PENDING = "operation_pending"
     OPERATION_IN_PROGRESS = "operation_in_progress"
     # Input validation
     INPUT_VALIDATION_ERROR = "input_validation_error"
@@ -45,8 +43,6 @@ class ResponseType(str, Enum):
     WEB_FETCH = "web_fetch"
     # Code execution
     BASH_EXEC = "bash_exec"
-    # Operation status check
-    OPERATION_STATUS = "operation_status"
     # Feature request types
     FEATURE_REQUEST_SEARCH = "feature_request_search"
     FEATURE_REQUEST_CREATED = "feature_request_created"
@@ -420,34 +416,6 @@ class BlockOutputResponse(ToolResponseBase):
 
 
 # Long-running operation models
-class OperationStartedResponse(ToolResponseBase):
-    """Response when a long-running operation has been started in the background.
-
-    This is returned immediately to the client while the operation continues
-    to execute. The user can close the tab and check back later.
-
-    The task_id can be used to reconnect to the SSE stream via
-    GET /chat/tasks/{task_id}/stream?last_idx=0
-    """
-
-    type: ResponseType = ResponseType.OPERATION_STARTED
-    operation_id: str
-    tool_name: str
-    task_id: str | None = None  # For SSE reconnection
-
-
-class OperationPendingResponse(ToolResponseBase):
-    """Response stored in chat history while a long-running operation is executing.
-
-    This is persisted to the database so users see a pending state when they
-    refresh before the operation completes.
-    """
-
-    type: ResponseType = ResponseType.OPERATION_PENDING
-    operation_id: str
-    tool_name: str
-
-
 class OperationInProgressResponse(ToolResponseBase):
     """Response when an operation is already in progress.
 
@@ -459,23 +427,6 @@ class OperationInProgressResponse(ToolResponseBase):
     tool_call_id: str
 
 
-class AsyncProcessingResponse(ToolResponseBase):
-    """Response when an operation has been delegated to async processing.
-
-    This is returned by tools when the external service accepts the request
-    for async processing (HTTP 202 Accepted). The Redis Streams completion
-    consumer will handle the result when the external service completes.
-
-    The status field is specifically "accepted" to allow the long-running tool
-    handler to detect this response and skip LLM continuation.
-    """
-
-    type: ResponseType = ResponseType.OPERATION_STARTED
-    status: str = "accepted"  # Must be "accepted" for detection
-    operation_id: str | None = None
-    task_id: str | None = None
-
-
 class WebFetchResponse(ToolResponseBase):
     """Response for web_fetch tool."""
 

autogpt_platform/backend/backend/copilot/tools/run_block.py

@@ -160,9 +160,10 @@ class RunBlockTool(BaseTool):
         logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
 
         creds_manager = IntegrationCredentialsManager()
-        matched_credentials, missing_credentials = (
-            await self._resolve_block_credentials(user_id, block, input_data)
-        )
+        (
+            matched_credentials,
+            missing_credentials,
+        ) = await self._resolve_block_credentials(user_id, block, input_data)
 
         # Get block schemas for details/validation
         try:

autogpt_platform/backend/backend/data/tally.py

@@ -0,0 +1,426 @@
+"""Tally form integration: cache submissions, match by email, extract business understanding."""
+
+import asyncio
+import json
+import logging
+from datetime import datetime, timezone
+from typing import Optional
+
+from openai import AsyncOpenAI
+
+from backend.data.redis_client import get_redis_async
+from backend.data.understanding import (
+    BusinessUnderstandingInput,
+    get_business_understanding,
+    upsert_business_understanding,
+)
+from backend.util.request import Requests
+from backend.util.settings import Settings
+
+logger = logging.getLogger(__name__)
+
+TALLY_API_BASE = "https://api.tally.so"
+_settings = Settings()
+TALLY_FORM_ID = _settings.secrets.tally_form_id
+
+# Redis key templates
+_EMAIL_INDEX_KEY = "tally:form:{form_id}:email_index"
+_QUESTIONS_KEY = "tally:form:{form_id}:questions"
+_LAST_FETCH_KEY = "tally:form:{form_id}:last_fetch"
+
+# TTLs — keep aligned so last_fetch never outlives the index
+_INDEX_TTL = 3600  # 1 hour
+_LAST_FETCH_TTL = 3600  # 1 hour (same as index)
+
+# Pagination
+_PAGE_LIMIT = 500
+_MAX_PAGES = 100
+
+# LLM extraction timeout (seconds)
+_LLM_TIMEOUT = 30
+
+
+def _mask_email(email: str) -> str:
+    """Mask an email for safe logging: 'alice@example.com' -> 'a***e@example.com'."""
+    try:
+        local, domain = email.rsplit("@", 1)
+        if len(local) <= 2:
+            masked_local = local[0] + "***"
+        else:
+            masked_local = local[0] + "***" + local[-1]
+        return f"{masked_local}@{domain}"
+    except (ValueError, IndexError):
+        return "***"
+
+
+async def _fetch_tally_page(
+    client: Requests,
+    form_id: str,
+    page: int,
+    limit: int = _PAGE_LIMIT,
+    start_date: Optional[str] = None,
+) -> dict:
+    """Fetch a single page of submissions from the Tally API."""
+    url = f"{TALLY_API_BASE}/forms/{form_id}/submissions?page={page}&limit={limit}"
+    if start_date:
+        url += f"&startDate={start_date}"
+
+    response = await client.get(url)
+    return response.json()
+
+
+def _make_tally_client(api_key: str) -> Requests:
+    """Create a Requests client configured for the Tally API."""
+    return Requests(
+        trusted_origins=[TALLY_API_BASE],
+        raise_for_status=True,
+        extra_headers={
+            "Authorization": f"Bearer {api_key}",
+            "Accept": "application/json",
+        },
+    )
+
+
+async def _fetch_all_submissions(
+    client: Requests,
+    form_id: str,
+    start_date: Optional[str] = None,
+    max_pages: int = _MAX_PAGES,
+) -> tuple[list[dict], list[dict]]:
+    """Paginate through all Tally submissions. Returns (questions, submissions)."""
+
+    questions: list[dict] = []
+    all_submissions: list[dict] = []
+    page = 1
+
+    while True:
+        data = await _fetch_tally_page(client, form_id, page, start_date=start_date)
+
+        if page == 1:
+            questions = data.get("questions", [])
+
+        submissions = data.get("submissions", [])
+        all_submissions.extend(submissions)
+
+        # Tally API uses `hasMore` for pagination
+        has_more = data.get("hasMore", False)
+        if not has_more:
+            break
+        if page >= max_pages:
+            total = data.get("totalNumberOfSubmissionsPerFilter", {}).get("all", "?")
+            logger.warning(
+                f"Tally: hit max page cap ({max_pages}) for form {form_id}, "
+                f"fetched {len(all_submissions)} of {total} total submissions"
+            )
+            break
+        page += 1
+
+    return questions, all_submissions
+
+
+def _build_email_index(
+    submissions: list[dict], questions: list[dict]
+) -> dict[str, dict]:
+    """Build an {email -> submission_data} index from submissions.
+
+    Scans question titles for email/contact fields to find the email answer.
+    """
+    # Find question IDs that are likely email fields
+    email_question_ids: list[str] = []
+    for q in questions:
+        label = (q.get("label") or q.get("title") or q.get("name") or "").lower()
+        q_type = (q.get("type") or "").lower()
+        if q_type in ("input_email", "email"):
+            email_question_ids.append(q["id"])
+        elif any(kw in label for kw in ("email", "e-mail", "contact")):
+            email_question_ids.append(q["id"])
+
+    index: dict[str, dict] = {}
+    for sub in submissions:
+        email = _extract_email_from_submission(sub, email_question_ids)
+        if email:
+            index[email.lower()] = {
+                "responses": sub.get("responses", sub.get("fields", [])),
+                "submitted_at": sub.get("submittedAt", sub.get("createdAt", "")),
+                "questions": sub.get("questions", []),
+            }
+    return index
+
+
+def _extract_email_from_submission(
+    submission: dict, email_question_ids: list[str]
+) -> Optional[str]:
+    """Extract email address from a submission by checking respondentEmail, then field responses."""
+    # Try respondent email first (Tally often includes this)
+    respondent_email = submission.get("respondentEmail")
+    if respondent_email:
+        return respondent_email
+
+    # Search through responses/fields for matching question IDs
+    responses = submission.get("responses", submission.get("fields", []))
+    if isinstance(responses, list):
+        for resp in responses:
+            q_id = resp.get("questionId") or resp.get("key") or resp.get("id")
+            if q_id in email_question_ids:
+                value = resp.get("value") or resp.get("answer")
+                if isinstance(value, str) and "@" in value:
+                    return value
+    elif isinstance(responses, dict):
+        for q_id in email_question_ids:
+            value = responses.get(q_id)
+            if isinstance(value, str) and "@" in value:
+                return value
+
+    return None
+
+
+async def _get_cached_index(
+    form_id: str,
+) -> tuple[Optional[dict], Optional[list]]:
+    """Return (email_index, questions) from Redis, or (None, None) on cache miss."""
+    redis = await get_redis_async()
+    index_key = _EMAIL_INDEX_KEY.format(form_id=form_id)
+    questions_key = _QUESTIONS_KEY.format(form_id=form_id)
+
+    raw_index = await redis.get(index_key)
+    raw_questions = await redis.get(questions_key)
+
+    if raw_index and raw_questions:
+        return json.loads(raw_index), json.loads(raw_questions)
+    return None, None
+
+
+async def _refresh_cache(form_id: str) -> tuple[dict, list]:
+    """Refresh the Tally submission cache. Uses incremental fetch when possible.
+
+    Returns (email_index, questions).
+    """
+    settings = Settings()
+    client = _make_tally_client(settings.secrets.tally_api_key)
+
+    redis = await get_redis_async()
+    last_fetch_key = _LAST_FETCH_KEY.format(form_id=form_id)
+    index_key = _EMAIL_INDEX_KEY.format(form_id=form_id)
+    questions_key = _QUESTIONS_KEY.format(form_id=form_id)
+
+    last_fetch = await redis.get(last_fetch_key)
+
+    if last_fetch:
+        # Try to load existing index for incremental merge
+        raw_existing = await redis.get(index_key)
+
+        if raw_existing is None:
+            # Index expired but last_fetch still present — fall back to full fetch
+            logger.info("Tally: last_fetch present but index missing, doing full fetch")
+            questions, submissions = await _fetch_all_submissions(client, form_id)
+            email_index = _build_email_index(submissions, questions)
+        else:
+            # Incremental fetch: only get new submissions since last fetch
+            logger.info(f"Tally incremental fetch since {last_fetch}")
+            questions, new_submissions = await _fetch_all_submissions(
+                client, form_id, start_date=last_fetch
+            )
+
+            existing_index: dict[str, dict] = json.loads(raw_existing)
+
+            if not questions:
+                raw_q = await redis.get(questions_key)
+                if raw_q:
+                    questions = json.loads(raw_q)
+
+            new_index = _build_email_index(new_submissions, questions)
+            existing_index.update(new_index)
+            email_index = existing_index
+    else:
+        # Full initial fetch
+        logger.info("Tally full initial fetch")
+        questions, submissions = await _fetch_all_submissions(client, form_id)
+        email_index = _build_email_index(submissions, questions)
+
+    # Store in Redis
+    now = datetime.now(timezone.utc).isoformat()
+    await redis.setex(index_key, _INDEX_TTL, json.dumps(email_index))
+    await redis.setex(questions_key, _INDEX_TTL, json.dumps(questions))
+    await redis.setex(last_fetch_key, _LAST_FETCH_TTL, now)
+
+    logger.info(f"Tally cache refreshed: {len(email_index)} emails indexed")
+    return email_index, questions
+
+
+async def find_submission_by_email(
+    form_id: str, email: str
+) -> Optional[tuple[dict, list]]:
+    """Look up a Tally submission by email. Uses cache when available.
+
+    Returns (submission_data, questions) or None.
+    """
+    email_lower = email.lower()
+
+    # Try cache first
+    email_index, questions = await _get_cached_index(form_id)
+    if email_index is not None and questions is not None:
+        sub = email_index.get(email_lower)
+        if sub is not None:
+            return sub, questions
+        return None
+
+    # Cache miss - refresh
+    email_index, questions = await _refresh_cache(form_id)
+    sub = email_index.get(email_lower)
+    if sub is not None:
+        return sub, questions
+    return None
+
+
+def format_submission_for_llm(submission: dict, questions: list[dict]) -> str:
+    """Format a submission as readable Q&A text for LLM consumption."""
+    # Build question ID -> title lookup
+    q_titles: dict[str, str] = {}
+    for q in questions:
+        q_id = q.get("id", "")
+        title = q.get("label") or q.get("title") or q.get("name") or f"Question {q_id}"
+        q_titles[q_id] = title
+
+    lines: list[str] = []
+    responses = submission.get("responses", [])
+
+    if isinstance(responses, list):
+        for resp in responses:
+            q_id = resp.get("questionId") or resp.get("key") or resp.get("id") or ""
+            title = q_titles.get(q_id, f"Question {q_id}")
+            value = resp.get("value") or resp.get("answer") or ""
+            lines.append(f"Q: {title}\nA: {_format_answer(value)}")
+    elif isinstance(responses, dict):
+        for q_id, value in responses.items():
+            title = q_titles.get(q_id, f"Question {q_id}")
+            lines.append(f"Q: {title}\nA: {_format_answer(value)}")
+
+    return "\n\n".join(lines)
+
+
+def _format_answer(value: object) -> str:
+    """Convert an answer value (str, list, dict, None) to a human-readable string."""
+    if value is None:
+        return "(no answer)"
+    if isinstance(value, list):
+        return ", ".join(str(v) for v in value)
+    if isinstance(value, dict):
+        parts = [f"{k}: {v}" for k, v in value.items() if v]
+        return "; ".join(parts) if parts else "(no answer)"
+    return str(value)
+
+
+_EXTRACTION_PROMPT = """\
+You are a business analyst. Given the following form submission data, extract structured business understanding information.
+
+Return a JSON object with ONLY the fields that can be confidently extracted. Use null for fields that cannot be determined.
+
+Fields:
+- user_name (string): the person's name
+- job_title (string): their job title
+- business_name (string): company/business name
+- industry (string): industry or sector
+- business_size (string): company size e.g. "1-10", "11-50", "51-200"
+- user_role (string): their role context e.g. "decision maker", "implementer"
+- key_workflows (list of strings): key business workflows
+- daily_activities (list of strings): daily activities performed
+- pain_points (list of strings): current pain points
+- bottlenecks (list of strings): process bottlenecks
+- manual_tasks (list of strings): manual/repetitive tasks
+- automation_goals (list of strings): desired automation goals
+- current_software (list of strings): software/tools currently used
+- existing_automation (list of strings): existing automations
+- additional_notes (string): any additional context
+
+Form data:
+"""
+
+_EXTRACTION_SUFFIX = "\n\nReturn ONLY valid JSON."
+
+
+async def extract_business_understanding(
+    formatted_text: str,
+) -> BusinessUnderstandingInput:
+    """Use an LLM to extract structured business understanding from form text.
+
+    Raises on timeout or unparseable response so the caller can handle it.
+    """
+    settings = Settings()
+    api_key = settings.secrets.open_router_api_key
+    client = AsyncOpenAI(api_key=api_key, base_url="https://openrouter.ai/api/v1")
+
+    try:
+        response = await asyncio.wait_for(
+            client.chat.completions.create(
+                model="openai/gpt-4o-mini",
+                messages=[
+                    {
+                        "role": "user",
+                        "content": f"{_EXTRACTION_PROMPT}{formatted_text}{_EXTRACTION_SUFFIX}",
+                    }
+                ],
+                response_format={"type": "json_object"},
+                temperature=0.0,
+            ),
+            timeout=_LLM_TIMEOUT,
+        )
+    except asyncio.TimeoutError:
+        logger.warning("Tally: LLM extraction timed out")
+        raise
+
+    raw = response.choices[0].message.content or "{}"
+    try:
+        data = json.loads(raw)
+    except json.JSONDecodeError:
+        logger.warning("Tally: LLM returned invalid JSON, skipping extraction")
+        raise
+
+    # Filter out null values before constructing
+    cleaned = {k: v for k, v in data.items() if v is not None}
+    return BusinessUnderstandingInput(**cleaned)
+
+
+async def populate_understanding_from_tally(user_id: str, email: str) -> None:
+    """Main orchestrator: check Tally for a matching submission and populate understanding.
+
+    Fire-and-forget safe — all exceptions are caught and logged.
+    """
+    try:
+        # Check if understanding already exists (idempotency)
+        existing = await get_business_understanding(user_id)
+        if existing is not None:
+            logger.debug(
+                f"Tally: user {user_id} already has business understanding, skipping"
+            )
+            return
+
+        # Check API key is configured
+        settings = Settings()
+        if not settings.secrets.tally_api_key:
+            logger.debug("Tally: no API key configured, skipping")
+            return
+
+        # Look up submission by email
+        masked = _mask_email(email)
+        result = await find_submission_by_email(TALLY_FORM_ID, email)
+        if result is None:
+            logger.debug(f"Tally: no submission found for {masked}")
+            return
+
+        submission, questions = result
+        logger.info(f"Tally: found submission for {masked}, extracting understanding")
+
+        # Format and extract
+        formatted = format_submission_for_llm(submission, questions)
+        if not formatted.strip():
+            logger.warning("Tally: formatted submission was empty, skipping")
+            return
+
+        understanding_input = await extract_business_understanding(formatted)
+
+        # Upsert into database
+        await upsert_business_understanding(user_id, understanding_input)
+        logger.info(f"Tally: successfully populated understanding for user {user_id}")
+
+    except Exception:
+        logger.exception(f"Tally: error populating understanding for user {user_id}")

autogpt_platform/backend/backend/data/tally_test.py

@@ -0,0 +1,589 @@
+"""Tests for backend.data.tally module."""
+
+import asyncio
+import json
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from backend.data.tally import (
+    _EXTRACTION_PROMPT,
+    _EXTRACTION_SUFFIX,
+    _build_email_index,
+    _format_answer,
+    _make_tally_client,
+    _mask_email,
+    _refresh_cache,
+    extract_business_understanding,
+    find_submission_by_email,
+    format_submission_for_llm,
+    populate_understanding_from_tally,
+)
+
+# ── Fixtures ──────────────────────────────────────────────────────────────────
+
+SAMPLE_QUESTIONS = [
+    {"id": "q1", "label": "What is your name?", "type": "INPUT_TEXT"},
+    {"id": "q2", "label": "Email address", "type": "INPUT_EMAIL"},
+    {"id": "q3", "label": "Company name", "type": "INPUT_TEXT"},
+    {"id": "q4", "label": "Industry", "type": "INPUT_TEXT"},
+]
+
+SAMPLE_SUBMISSIONS = [
+    {
+        "respondentEmail": None,
+        "responses": [
+            {"questionId": "q1", "value": "Alice Smith"},
+            {"questionId": "q2", "value": "alice@example.com"},
+            {"questionId": "q3", "value": "Acme Corp"},
+            {"questionId": "q4", "value": "Technology"},
+        ],
+        "submittedAt": "2025-01-15T10:00:00Z",
+    },
+    {
+        "respondentEmail": "bob@example.com",
+        "responses": [
+            {"questionId": "q1", "value": "Bob Jones"},
+            {"questionId": "q2", "value": "bob@example.com"},
+            {"questionId": "q3", "value": "Bob's Burgers"},
+            {"questionId": "q4", "value": "Food"},
+        ],
+        "submittedAt": "2025-01-16T10:00:00Z",
+    },
+]
+
+
+# ── _build_email_index ────────────────────────────────────────────────────────
+
+
+def test_build_email_index():
+    index = _build_email_index(SAMPLE_SUBMISSIONS, SAMPLE_QUESTIONS)
+    assert "alice@example.com" in index
+    assert "bob@example.com" in index
+    assert len(index) == 2
+
+
+def test_build_email_index_case_insensitive():
+    submissions = [
+        {
+            "respondentEmail": None,
+            "responses": [
+                {"questionId": "q2", "value": "Alice@Example.COM"},
+            ],
+            "submittedAt": "2025-01-15T10:00:00Z",
+        },
+    ]
+    index = _build_email_index(submissions, SAMPLE_QUESTIONS)
+    assert "alice@example.com" in index
+    assert "Alice@Example.COM" not in index
+
+
+def test_build_email_index_empty():
+    index = _build_email_index([], SAMPLE_QUESTIONS)
+    assert index == {}
+
+
+def test_build_email_index_no_email_field():
+    questions = [{"id": "q1", "label": "Name", "type": "INPUT_TEXT"}]
+    submissions = [
+        {
+            "responses": [{"questionId": "q1", "value": "Alice"}],
+            "submittedAt": "2025-01-15T10:00:00Z",
+        }
+    ]
+    index = _build_email_index(submissions, questions)
+    assert index == {}
+
+
+def test_build_email_index_respondent_email():
+    """respondentEmail takes precedence over field scanning."""
+    submissions = [
+        {
+            "respondentEmail": "direct@example.com",
+            "responses": [
+                {"questionId": "q2", "value": "field@example.com"},
+            ],
+            "submittedAt": "2025-01-15T10:00:00Z",
+        }
+    ]
+    index = _build_email_index(submissions, SAMPLE_QUESTIONS)
+    assert "direct@example.com" in index
+    assert "field@example.com" not in index
+
+
+# ── format_submission_for_llm ─────────────────────────────────────────────────
+
+
+def test_format_submission_for_llm():
+    submission = {
+        "responses": [
+            {"questionId": "q1", "value": "Alice Smith"},
+            {"questionId": "q3", "value": "Acme Corp"},
+        ],
+    }
+    result = format_submission_for_llm(submission, SAMPLE_QUESTIONS)
+    assert "Q: What is your name?" in result
+    assert "A: Alice Smith" in result
+    assert "Q: Company name" in result
+    assert "A: Acme Corp" in result
+
+
+def test_format_submission_for_llm_dict_responses():
+    submission = {
+        "responses": {
+            "q1": "Alice Smith",
+            "q3": "Acme Corp",
+        },
+    }
+    result = format_submission_for_llm(submission, SAMPLE_QUESTIONS)
+    assert "A: Alice Smith" in result
+    assert "A: Acme Corp" in result
+
+
+def test_format_answer_types():
+    assert _format_answer(None) == "(no answer)"
+    assert _format_answer("hello") == "hello"
+    assert _format_answer(["a", "b"]) == "a, b"
+    assert _format_answer({"key": "val"}) == "key: val"
+    assert _format_answer(42) == "42"
+
+
+# ── find_submission_by_email ──────────────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_find_submission_by_email_cache_hit():
+    cached_index = {
+        "alice@example.com": {"responses": [], "submitted_at": "2025-01-15"},
+    }
+    cached_questions = SAMPLE_QUESTIONS
+
+    with patch(
+        "backend.data.tally._get_cached_index",
+        new_callable=AsyncMock,
+        return_value=(cached_index, cached_questions),
+    ) as mock_cache:
+        result = await find_submission_by_email("form123", "alice@example.com")
+
+    mock_cache.assert_awaited_once_with("form123")
+    assert result is not None
+    sub, questions = result
+    assert sub["submitted_at"] == "2025-01-15"
+
+
+@pytest.mark.asyncio
+async def test_find_submission_by_email_cache_miss():
+    refreshed_index = {
+        "alice@example.com": {"responses": [], "submitted_at": "2025-01-15"},
+    }
+
+    with (
+        patch(
+            "backend.data.tally._get_cached_index",
+            new_callable=AsyncMock,
+            return_value=(None, None),
+        ),
+        patch(
+            "backend.data.tally._refresh_cache",
+            new_callable=AsyncMock,
+            return_value=(refreshed_index, SAMPLE_QUESTIONS),
+        ) as mock_refresh,
+    ):
+        result = await find_submission_by_email("form123", "alice@example.com")
+
+    mock_refresh.assert_awaited_once_with("form123")
+    assert result is not None
+
+
+@pytest.mark.asyncio
+async def test_find_submission_by_email_no_match():
+    cached_index = {
+        "alice@example.com": {"responses": [], "submitted_at": "2025-01-15"},
+    }
+
+    with patch(
+        "backend.data.tally._get_cached_index",
+        new_callable=AsyncMock,
+        return_value=(cached_index, SAMPLE_QUESTIONS),
+    ):
+        result = await find_submission_by_email("form123", "unknown@example.com")
+
+    assert result is None
+
+
+# ── populate_understanding_from_tally ─────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_populate_understanding_skips_existing():
+    """If user already has understanding, skip entirely."""
+    mock_understanding = MagicMock()
+
+    with (
+        patch(
+            "backend.data.tally.get_business_understanding",
+            new_callable=AsyncMock,
+            return_value=mock_understanding,
+        ) as mock_get,
+        patch(
+            "backend.data.tally.find_submission_by_email",
+            new_callable=AsyncMock,
+        ) as mock_find,
+    ):
+        await populate_understanding_from_tally("user-1", "test@example.com")
+
+    mock_get.assert_awaited_once_with("user-1")
+    mock_find.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_populate_understanding_skips_no_api_key():
+    """If no Tally API key, skip gracefully."""
+    mock_settings = MagicMock()
+    mock_settings.secrets.tally_api_key = ""
+
+    with (
+        patch(
+            "backend.data.tally.get_business_understanding",
+            new_callable=AsyncMock,
+            return_value=None,
+        ),
+        patch("backend.data.tally.Settings", return_value=mock_settings),
+        patch(
+            "backend.data.tally.find_submission_by_email",
+            new_callable=AsyncMock,
+        ) as mock_find,
+    ):
+        await populate_understanding_from_tally("user-1", "test@example.com")
+
+    mock_find.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_populate_understanding_handles_errors():
+    """Must never raise, even on unexpected errors."""
+    with patch(
+        "backend.data.tally.get_business_understanding",
+        new_callable=AsyncMock,
+        side_effect=RuntimeError("DB down"),
+    ):
+        # Should not raise
+        await populate_understanding_from_tally("user-1", "test@example.com")
+
+
+@pytest.mark.asyncio
+async def test_populate_understanding_full_flow():
+    """Happy path: no existing understanding, finds submission, extracts, upserts."""
+    mock_settings = MagicMock()
+    mock_settings.secrets.tally_api_key = "test-key"
+
+    submission = {
+        "responses": [
+            {"questionId": "q1", "value": "Alice"},
+            {"questionId": "q3", "value": "Acme"},
+        ],
+    }
+    mock_input = MagicMock()
+
+    with (
+        patch(
+            "backend.data.tally.get_business_understanding",
+            new_callable=AsyncMock,
+            return_value=None,
+        ),
+        patch("backend.data.tally.Settings", return_value=mock_settings),
+        patch(
+            "backend.data.tally.find_submission_by_email",
+            new_callable=AsyncMock,
+            return_value=(submission, SAMPLE_QUESTIONS),
+        ),
+        patch(
+            "backend.data.tally.extract_business_understanding",
+            new_callable=AsyncMock,
+            return_value=mock_input,
+        ) as mock_extract,
+        patch(
+            "backend.data.tally.upsert_business_understanding",
+            new_callable=AsyncMock,
+        ) as mock_upsert,
+    ):
+        await populate_understanding_from_tally("user-1", "alice@example.com")
+
+    mock_extract.assert_awaited_once()
+    mock_upsert.assert_awaited_once_with("user-1", mock_input)
+
+
+@pytest.mark.asyncio
+async def test_populate_understanding_handles_llm_timeout():
+    """LLM timeout is caught and doesn't raise."""
+    import asyncio
+
+    mock_settings = MagicMock()
+    mock_settings.secrets.tally_api_key = "test-key"
+
+    submission = {
+        "responses": [{"questionId": "q1", "value": "Alice"}],
+    }
+
+    with (
+        patch(
+            "backend.data.tally.get_business_understanding",
+            new_callable=AsyncMock,
+            return_value=None,
+        ),
+        patch("backend.data.tally.Settings", return_value=mock_settings),
+        patch(
+            "backend.data.tally.find_submission_by_email",
+            new_callable=AsyncMock,
+            return_value=(submission, SAMPLE_QUESTIONS),
+        ),
+        patch(
+            "backend.data.tally.extract_business_understanding",
+            new_callable=AsyncMock,
+            side_effect=asyncio.TimeoutError(),
+        ),
+        patch(
+            "backend.data.tally.upsert_business_understanding",
+            new_callable=AsyncMock,
+        ) as mock_upsert,
+    ):
+        await populate_understanding_from_tally("user-1", "alice@example.com")
+
+    mock_upsert.assert_not_awaited()
+
+
+# ── _mask_email ───────────────────────────────────────────────────────────────
+
+
+def test_mask_email():
+    assert _mask_email("alice@example.com") == "a***e@example.com"
+    assert _mask_email("ab@example.com") == "a***@example.com"
+    assert _mask_email("a@example.com") == "a***@example.com"
+
+
+def test_mask_email_invalid():
+    assert _mask_email("no-at-sign") == "***"
+
+
+# ── Prompt construction (curly-brace safety) ─────────────────────────────────
+
+
+def test_extraction_prompt_safe_with_curly_braces():
+    """User content with curly braces must not break prompt construction.
+
+    Previously _EXTRACTION_PROMPT.format(submission_text=...) would raise
+    KeyError/ValueError if the user text contained { or }.
+    """
+    text_with_braces = "Q: What tools do you use?\nA: We use {Slack} and {{Jira}}"
+    # This must not raise — the old .format() call would fail here
+    prompt = f"{_EXTRACTION_PROMPT}{text_with_braces}{_EXTRACTION_SUFFIX}"
+    assert text_with_braces in prompt
+    assert prompt.startswith("You are a business analyst.")
+    assert prompt.endswith("Return ONLY valid JSON.")
+
+
+def test_extraction_prompt_no_format_placeholders():
+    """_EXTRACTION_PROMPT must not contain Python format placeholders."""
+    assert "{submission_text}" not in _EXTRACTION_PROMPT
+    # Ensure no stray single-brace placeholders
+    # (double braces {{ are fine — they're literal in format strings)
+    import re
+
+    single_braces = re.findall(r"(?<!\{)\{[^{].*?\}(?!\})", _EXTRACTION_PROMPT)
+    assert single_braces == [], f"Found format placeholders: {single_braces}"
+
+
+# ── extract_business_understanding ────────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_extract_business_understanding_success():
+    """Happy path: LLM returns valid JSON that maps to BusinessUnderstandingInput."""
+    mock_choice = MagicMock()
+    mock_choice.message.content = json.dumps(
+        {
+            "user_name": "Alice",
+            "business_name": "Acme Corp",
+            "industry": "Technology",
+            "pain_points": ["manual reporting"],
+        }
+    )
+    mock_response = MagicMock()
+    mock_response.choices = [mock_choice]
+
+    mock_client = AsyncMock()
+    mock_client.chat.completions.create.return_value = mock_response
+
+    with patch("backend.data.tally.AsyncOpenAI", return_value=mock_client):
+        result = await extract_business_understanding("Q: Name?\nA: Alice")
+
+    assert result.user_name == "Alice"
+    assert result.business_name == "Acme Corp"
+    assert result.industry == "Technology"
+    assert result.pain_points == ["manual reporting"]
+
+
+@pytest.mark.asyncio
+async def test_extract_business_understanding_filters_nulls():
+    """Null values from LLM should be excluded from the result."""
+    mock_choice = MagicMock()
+    mock_choice.message.content = json.dumps(
+        {"user_name": "Alice", "business_name": None, "industry": None}
+    )
+    mock_response = MagicMock()
+    mock_response.choices = [mock_choice]
+
+    mock_client = AsyncMock()
+    mock_client.chat.completions.create.return_value = mock_response
+
+    with patch("backend.data.tally.AsyncOpenAI", return_value=mock_client):
+        result = await extract_business_understanding("Q: Name?\nA: Alice")
+
+    assert result.user_name == "Alice"
+    assert result.business_name is None
+    assert result.industry is None
+
+
+@pytest.mark.asyncio
+async def test_extract_business_understanding_invalid_json():
+    """Invalid JSON from LLM should raise JSONDecodeError."""
+    mock_choice = MagicMock()
+    mock_choice.message.content = "not valid json {"
+    mock_response = MagicMock()
+    mock_response.choices = [mock_choice]
+
+    mock_client = AsyncMock()
+    mock_client.chat.completions.create.return_value = mock_response
+
+    with (
+        patch("backend.data.tally.AsyncOpenAI", return_value=mock_client),
+        pytest.raises(json.JSONDecodeError),
+    ):
+        await extract_business_understanding("Q: Name?\nA: Alice")
+
+
+@pytest.mark.asyncio
+async def test_extract_business_understanding_timeout():
+    """LLM timeout should propagate as asyncio.TimeoutError."""
+    mock_client = AsyncMock()
+    mock_client.chat.completions.create.side_effect = asyncio.TimeoutError()
+
+    with (
+        patch("backend.data.tally.AsyncOpenAI", return_value=mock_client),
+        patch("backend.data.tally._LLM_TIMEOUT", 0.001),
+        pytest.raises(asyncio.TimeoutError),
+    ):
+        await extract_business_understanding("Q: Name?\nA: Alice")
+
+
+# ── _refresh_cache ───────────────────────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_refresh_cache_full_fetch():
+    """First fetch (no last_fetch in Redis) should do a full fetch and store in Redis."""
+    mock_settings = MagicMock()
+    mock_settings.secrets.tally_api_key = "test-key"
+
+    mock_redis = AsyncMock()
+    mock_redis.get.return_value = None  # No last_fetch, no cached index
+
+    questions = SAMPLE_QUESTIONS
+    submissions = SAMPLE_SUBMISSIONS
+
+    with (
+        patch("backend.data.tally.Settings", return_value=mock_settings),
+        patch(
+            "backend.data.tally.get_redis_async",
+            new_callable=AsyncMock,
+            return_value=mock_redis,
+        ),
+        patch(
+            "backend.data.tally._fetch_all_submissions",
+            new_callable=AsyncMock,
+            return_value=(questions, submissions),
+        ) as mock_fetch,
+    ):
+        index, returned_questions = await _refresh_cache("form123")
+
+    mock_fetch.assert_awaited_once()
+    assert "alice@example.com" in index
+    assert "bob@example.com" in index
+    assert returned_questions == questions
+    # Verify Redis setex was called for index, questions, and last_fetch
+    assert mock_redis.setex.await_count == 3
+
+
+@pytest.mark.asyncio
+async def test_refresh_cache_incremental_fetch():
+    """When last_fetch and index both exist, should do incremental fetch and merge."""
+    mock_settings = MagicMock()
+    mock_settings.secrets.tally_api_key = "test-key"
+
+    existing_index = {
+        "old@example.com": {"responses": [], "submitted_at": "2025-01-01"}
+    }
+
+    mock_redis = AsyncMock()
+
+    def mock_get(key):
+        if "last_fetch" in key:
+            return "2025-01-14T00:00:00Z"
+        if "email_index" in key:
+            return json.dumps(existing_index)
+        if "questions" in key:
+            return json.dumps(SAMPLE_QUESTIONS)
+        return None
+
+    mock_redis.get.side_effect = mock_get
+
+    new_submissions = [SAMPLE_SUBMISSIONS[0]]  # Just Alice
+
+    with (
+        patch("backend.data.tally.Settings", return_value=mock_settings),
+        patch(
+            "backend.data.tally.get_redis_async",
+            new_callable=AsyncMock,
+            return_value=mock_redis,
+        ),
+        patch(
+            "backend.data.tally._fetch_all_submissions",
+            new_callable=AsyncMock,
+            return_value=(SAMPLE_QUESTIONS, new_submissions),
+        ),
+    ):
+        index, _ = await _refresh_cache("form123")
+
+    # Should contain both old and new entries
+    assert "old@example.com" in index
+    assert "alice@example.com" in index
+
+
+# ── _make_tally_client ───────────────────────────────────────────────────────
+
+
+def test_make_tally_client_returns_configured_client():
+    """_make_tally_client should create a Requests client with auth headers."""
+    client = _make_tally_client("test-api-key")
+    assert client.extra_headers is not None
+    assert client.extra_headers.get("Authorization") == "Bearer test-api-key"
+
+
+@pytest.mark.asyncio
+async def test_fetch_tally_page_uses_provided_client():
+    """_fetch_tally_page should use the passed client, not create its own."""
+    from backend.data.tally import _fetch_tally_page
+
+    mock_response = MagicMock()
+    mock_response.json.return_value = {"submissions": [], "questions": []}
+
+    mock_client = AsyncMock()
+    mock_client.get.return_value = mock_response
+
+    result = await _fetch_tally_page(mock_client, "form123", page=1)
+
+    mock_client.get.assert_awaited_once()
+    call_url = mock_client.get.call_args[0][0]
+    assert "form123" in call_url
+    assert "page=1" in call_url
+    assert result == {"submissions": [], "questions": []}

autogpt_platform/backend/backend/util/settings.py

@@ -372,8 +372,8 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
         description="The port for the Agent Generator service",
     )
     agentgenerator_timeout: int = Field(
-        default=600,
-        description="The timeout in seconds for Agent Generator service requests (includes retries for rate limits)",
+        default=30,
+        description="The timeout in seconds for individual Agent Generator HTTP requests (submit and poll)",
     )
     agentgenerator_use_dummy: bool = Field(
         default=False,
@@ -691,6 +691,15 @@ class Secrets(UpdateTrackingModel["Secrets"], BaseSettings):
 
     screenshotone_api_key: str = Field(default="", description="ScreenshotOne API Key")
 
+    tally_api_key: str = Field(
+        default="",
+        description="Tally API key for form submission lookup on signup",
+    )
+    tally_form_id: str = Field(
+        default="npGe0q",
+        description="Tally form ID for signup business understanding form",
+    )
+
     apollo_api_key: str = Field(default="", description="Apollo API Key")
     smartlead_api_key: str = Field(default="", description="SmartLead API Key")
     zerobounce_api_key: str = Field(default="", description="ZeroBounce API Key")

autogpt_platform/backend/test/agent_generator/test_core_integration.py

@@ -109,7 +109,7 @@ class TestGenerateAgent:
             instructions = {"type": "instructions", "steps": ["Step 1"]}
             result = await core.generate_agent(instructions)
 
-            mock_external.assert_called_once_with(instructions, None, None, None)
+            mock_external.assert_called_once_with(instructions, None)
             assert result is not None
             assert result["name"] == "Test Agent"
             assert "id" in result
@@ -173,9 +173,7 @@ class TestGenerateAgentPatch:
             current_agent = {"nodes": [], "links": []}
             result = await core.generate_agent_patch("Add a node", current_agent)
 
-            mock_external.assert_called_once_with(
-                "Add a node", current_agent, None, None, None
-            )
+            mock_external.assert_called_once_with("Add a node", current_agent, None)
             assert result == expected_result
 
     @pytest.mark.asyncio

autogpt_platform/backend/test/agent_generator/test_service.py

@@ -2,7 +2,7 @@
 Tests for the Agent Generator external service client.
 
 This test suite verifies the external Agent Generator service integration,
-including service detection, API calls, and error handling.
+including service detection, async polling, and error handling.
 """
 
 from unittest.mock import AsyncMock, MagicMock, patch
@@ -49,6 +49,292 @@ class TestServiceConfiguration:
             assert url == "http://agent-generator.local:8000"
 
 
+class TestSubmitAndPoll:
+    """Test the _submit_and_poll helper that handles async job polling."""
+
+    def setup_method(self):
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_successful_submit_and_poll(self):
+        """Test normal submit -> poll -> completed flow."""
+        submit_resp = MagicMock()
+        submit_resp.json.return_value = {"job_id": "job-123", "status": "accepted"}
+        submit_resp.raise_for_status = MagicMock()
+
+        poll_resp = MagicMock()
+        poll_resp.json.return_value = {
+            "job_id": "job-123",
+            "status": "completed",
+            "result": {"type": "instructions", "steps": ["Step 1"]},
+        }
+        poll_resp.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = submit_resp
+        mock_client.get.return_value = poll_resp
+
+        with (
+            patch.object(service, "_get_client", return_value=mock_client),
+            patch("asyncio.sleep", new_callable=AsyncMock),
+        ):
+            result = await service._submit_and_poll("/api/test", {"key": "value"})
+
+        assert result == {"type": "instructions", "steps": ["Step 1"]}
+        mock_client.post.assert_called_once_with("/api/test", json={"key": "value"})
+        mock_client.get.assert_called_once_with("/api/jobs/job-123")
+
+    @pytest.mark.asyncio
+    async def test_poll_returns_failed_job(self):
+        """Test submit -> poll -> failed flow."""
+        submit_resp = MagicMock()
+        submit_resp.json.return_value = {"job_id": "job-456", "status": "accepted"}
+        submit_resp.raise_for_status = MagicMock()
+
+        poll_resp = MagicMock()
+        poll_resp.json.return_value = {
+            "job_id": "job-456",
+            "status": "failed",
+            "error": "Generation failed",
+        }
+        poll_resp.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = submit_resp
+        mock_client.get.return_value = poll_resp
+
+        with (
+            patch.object(service, "_get_client", return_value=mock_client),
+            patch("asyncio.sleep", new_callable=AsyncMock),
+        ):
+            result = await service._submit_and_poll("/api/test", {})
+
+        assert result["type"] == "error"
+        assert result["error_type"] == "job_failed"
+        assert "Generation failed" in result["error"]
+
+    @pytest.mark.asyncio
+    async def test_submit_http_error(self):
+        """Test HTTP error during job submission."""
+        mock_response = MagicMock()
+        mock_response.status_code = 500
+        mock_client = AsyncMock()
+        mock_client.post.side_effect = httpx.HTTPStatusError(
+            "Server error", request=MagicMock(), response=mock_response
+        )
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service._submit_and_poll("/api/test", {})
+
+        assert result["type"] == "error"
+        assert result["error_type"] == "http_error"
+
+    @pytest.mark.asyncio
+    async def test_submit_connection_error(self):
+        """Test connection error during job submission."""
+        mock_client = AsyncMock()
+        mock_client.post.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service._submit_and_poll("/api/test", {})
+
+        assert result["type"] == "error"
+        assert result["error_type"] == "connection_error"
+
+    @pytest.mark.asyncio
+    async def test_no_job_id_in_submit_response(self):
+        """Test submit response missing job_id."""
+        submit_resp = MagicMock()
+        submit_resp.json.return_value = {"status": "accepted"}  # no job_id
+        submit_resp.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = submit_resp
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service._submit_and_poll("/api/test", {})
+
+        assert result["type"] == "error"
+        assert result["error_type"] == "invalid_response"
+
+    @pytest.mark.asyncio
+    async def test_poll_retries_on_transient_network_error(self):
+        """Test that transient network errors during polling are retried."""
+        submit_resp = MagicMock()
+        submit_resp.json.return_value = {"job_id": "job-789"}
+        submit_resp.raise_for_status = MagicMock()
+
+        ok_poll_resp = MagicMock()
+        ok_poll_resp.json.return_value = {
+            "job_id": "job-789",
+            "status": "completed",
+            "result": {"data": "ok"},
+        }
+        ok_poll_resp.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = submit_resp
+        # First poll fails with transient error, second succeeds
+        mock_client.get.side_effect = [
+            httpx.RequestError("transient"),
+            ok_poll_resp,
+        ]
+
+        with (
+            patch.object(service, "_get_client", return_value=mock_client),
+            patch("asyncio.sleep", new_callable=AsyncMock),
+        ):
+            result = await service._submit_and_poll("/api/test", {})
+
+        assert result == {"data": "ok"}
+        assert mock_client.get.call_count == 2
+
+    @pytest.mark.asyncio
+    async def test_poll_returns_404_for_expired_job(self):
+        """Test that 404 during polling returns job_not_found error."""
+        submit_resp = MagicMock()
+        submit_resp.json.return_value = {"job_id": "job-expired"}
+        submit_resp.raise_for_status = MagicMock()
+
+        mock_404_response = MagicMock()
+        mock_404_response.status_code = 404
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = submit_resp
+        mock_client.get.side_effect = httpx.HTTPStatusError(
+            "Not Found", request=MagicMock(), response=mock_404_response
+        )
+
+        with (
+            patch.object(service, "_get_client", return_value=mock_client),
+            patch("asyncio.sleep", new_callable=AsyncMock),
+        ):
+            result = await service._submit_and_poll("/api/test", {})
+
+        assert result["type"] == "error"
+        assert result["error_type"] == "job_not_found"
+
+    @pytest.mark.asyncio
+    async def test_poll_retries_on_transient_http_status(self):
+        """Test that transient HTTP status codes (429, 503, etc.) are retried."""
+        submit_resp = MagicMock()
+        submit_resp.json.return_value = {"job_id": "job-transient"}
+        submit_resp.raise_for_status = MagicMock()
+
+        mock_429_response = MagicMock()
+        mock_429_response.status_code = 429
+
+        ok_poll_resp = MagicMock()
+        ok_poll_resp.json.return_value = {
+            "job_id": "job-transient",
+            "status": "completed",
+            "result": {"data": "recovered"},
+        }
+        ok_poll_resp.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = submit_resp
+        mock_client.get.side_effect = [
+            httpx.HTTPStatusError(
+                "Too Many Requests", request=MagicMock(), response=mock_429_response
+            ),
+            ok_poll_resp,
+        ]
+
+        with (
+            patch.object(service, "_get_client", return_value=mock_client),
+            patch("asyncio.sleep", new_callable=AsyncMock),
+        ):
+            result = await service._submit_and_poll("/api/test", {})
+
+        assert result == {"data": "recovered"}
+        assert mock_client.get.call_count == 2
+
+    @pytest.mark.asyncio
+    async def test_poll_does_not_retry_non_transient_http_status(self):
+        """Test that non-transient HTTP status codes (e.g. 500) fail immediately."""
+        submit_resp = MagicMock()
+        submit_resp.json.return_value = {"job_id": "job-500"}
+        submit_resp.raise_for_status = MagicMock()
+
+        mock_500_response = MagicMock()
+        mock_500_response.status_code = 500
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = submit_resp
+        mock_client.get.side_effect = httpx.HTTPStatusError(
+            "Internal Server Error", request=MagicMock(), response=mock_500_response
+        )
+
+        with (
+            patch.object(service, "_get_client", return_value=mock_client),
+            patch("asyncio.sleep", new_callable=AsyncMock),
+        ):
+            result = await service._submit_and_poll("/api/test", {})
+
+        assert result["type"] == "error"
+        assert result["error_type"] == "http_error"
+        assert mock_client.get.call_count == 1
+
+    @pytest.mark.asyncio
+    async def test_poll_timeout(self):
+        """Test that polling times out after MAX_POLL_TIME_SECONDS."""
+        submit_resp = MagicMock()
+        submit_resp.json.return_value = {"job_id": "job-slow"}
+        submit_resp.raise_for_status = MagicMock()
+
+        running_resp = MagicMock()
+        running_resp.json.return_value = {"job_id": "job-slow", "status": "running"}
+        running_resp.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = submit_resp
+        mock_client.get.return_value = running_resp
+
+        # Simulate time passing: first call returns 0.0 (start), then jumps past limit
+        monotonic_values = iter([0.0, 0.0, 100.0])
+
+        with (
+            patch.object(service, "_get_client", return_value=mock_client),
+            patch.object(service, "MAX_POLL_TIME_SECONDS", 50.0),
+            patch.object(service, "POLL_INTERVAL_SECONDS", 0.01),
+            patch("asyncio.sleep", new_callable=AsyncMock),
+            patch("backend.copilot.tools.agent_generator.service.time") as mock_time,
+        ):
+            mock_time.monotonic.side_effect = monotonic_values
+            result = await service._submit_and_poll("/api/test", {})
+
+        assert result["type"] == "error"
+        assert result["error_type"] == "timeout"
+
+    @pytest.mark.asyncio
+    async def test_poll_gives_up_after_consecutive_transient_errors(self):
+        """Test that polling gives up after MAX_CONSECUTIVE_POLL_ERRORS."""
+        submit_resp = MagicMock()
+        submit_resp.json.return_value = {"job_id": "job-flaky"}
+        submit_resp.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = submit_resp
+        mock_client.get.side_effect = httpx.RequestError("network down")
+
+        # Ensure monotonic always returns 0 so timeout doesn't kick in
+        with (
+            patch.object(service, "_get_client", return_value=mock_client),
+            patch.object(service, "MAX_POLL_TIME_SECONDS", 9999.0),
+            patch.object(service, "POLL_INTERVAL_SECONDS", 0.01),
+            patch("asyncio.sleep", new_callable=AsyncMock),
+            patch("backend.copilot.tools.agent_generator.service.time") as mock_time,
+        ):
+            mock_time.monotonic.return_value = 0.0
+            result = await service._submit_and_poll("/api/test", {})
+
+        assert result["type"] == "error"
+        assert result["error_type"] == "poll_error"
+        assert mock_client.get.call_count == service.MAX_CONSECUTIVE_POLL_ERRORS
+
+
 class TestDecomposeGoalExternal:
     """Test decompose_goal_external function."""
 
@@ -60,40 +346,37 @@ class TestDecomposeGoalExternal:
     @pytest.mark.asyncio
     async def test_decompose_goal_returns_instructions(self):
         """Test successful decomposition returning instructions."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "type": "instructions",
-            "steps": ["Step 1", "Step 2"],
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {
+                "type": "instructions",
+                "steps": ["Step 1", "Step 2"],
+            }
             result = await service.decompose_goal_external("Build a chatbot")
 
         assert result == {"type": "instructions", "steps": ["Step 1", "Step 2"]}
-        mock_client.post.assert_called_once_with(
-            "/api/decompose-description", json={"description": "Build a chatbot"}
+        mock_poll.assert_called_once_with(
+            "/api/decompose-description",
+            {"description": "Build a chatbot"},
         )
 
     @pytest.mark.asyncio
     async def test_decompose_goal_returns_clarifying_questions(self):
         """Test decomposition returning clarifying questions."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "type": "clarifying_questions",
-            "questions": ["What platform?", "What language?"],
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {
+                "type": "clarifying_questions",
+                "questions": ["What platform?", "What language?"],
+            }
             result = await service.decompose_goal_external("Build something")
 
         assert result == {
@@ -104,18 +387,13 @@ class TestDecomposeGoalExternal:
     @pytest.mark.asyncio
     async def test_decompose_goal_with_context(self):
         """Test decomposition with additional context enriched into description."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "type": "instructions",
-            "steps": ["Step 1"],
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {"type": "instructions", "steps": ["Step 1"]}
             await service.decompose_goal_external(
                 "Build a chatbot", context="Use Python"
             )
@@ -123,27 +401,25 @@ class TestDecomposeGoalExternal:
         expected_description = (
             "Build a chatbot\n\nAdditional context from user:\nUse Python"
         )
-        mock_client.post.assert_called_once_with(
+        mock_poll.assert_called_once_with(
             "/api/decompose-description",
-            json={"description": expected_description},
+            {"description": expected_description},
         )
 
     @pytest.mark.asyncio
     async def test_decompose_goal_returns_unachievable_goal(self):
         """Test decomposition returning unachievable goal response."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "type": "unachievable_goal",
-            "reason": "Cannot do X",
-            "suggested_goal": "Try Y instead",
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {
+                "type": "unachievable_goal",
+                "reason": "Cannot do X",
+                "suggested_goal": "Try Y instead",
+            }
             result = await service.decompose_goal_external("Do something impossible")
 
         assert result == {
@@ -153,58 +429,40 @@ class TestDecomposeGoalExternal:
         }
 
     @pytest.mark.asyncio
-    async def test_decompose_goal_handles_http_error(self):
-        """Test decomposition handles HTTP errors gracefully."""
-        mock_response = MagicMock()
-        mock_response.status_code = 500
-        mock_client = AsyncMock()
-        mock_client.post.side_effect = httpx.HTTPStatusError(
-            "Server error", request=MagicMock(), response=mock_response
-        )
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+    async def test_decompose_goal_handles_poll_error(self):
+        """Test that errors from _submit_and_poll are passed through."""
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {
+                "type": "error",
+                "error": "HTTP error calling Agent Generator: Server error",
+                "error_type": "http_error",
+            }
             result = await service.decompose_goal_external("Build a chatbot")
 
         assert result is not None
         assert result.get("type") == "error"
         assert result.get("error_type") == "http_error"
-        assert "Server error" in result.get("error", "")
 
     @pytest.mark.asyncio
-    async def test_decompose_goal_handles_request_error(self):
-        """Test decomposition handles request errors gracefully."""
-        mock_client = AsyncMock()
-        mock_client.post.side_effect = httpx.RequestError("Connection failed")
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+    async def test_decompose_goal_handles_unexpected_exception(self):
+        """Test that unexpected exceptions are caught and returned as errors."""
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.side_effect = RuntimeError("unexpected")
             result = await service.decompose_goal_external("Build a chatbot")
 
         assert result is not None
         assert result.get("type") == "error"
-        assert result.get("error_type") == "connection_error"
-        assert "Connection failed" in result.get("error", "")
-
-    @pytest.mark.asyncio
-    async def test_decompose_goal_handles_service_error(self):
-        """Test decomposition handles service returning error."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": False,
-            "error": "Internal error",
-            "error_type": "internal_error",
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
-            result = await service.decompose_goal_external("Build a chatbot")
-
-        assert result is not None
-        assert result.get("type") == "error"
-        assert result.get("error") == "Internal error"
-        assert result.get("error_type") == "internal_error"
+        assert result.get("error_type") == "unexpected_error"
 
 
 class TestGenerateAgentExternal:
@@ -223,39 +481,59 @@ class TestGenerateAgentExternal:
             "nodes": [],
             "links": [],
         }
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "agent_json": agent_json,
-        }
-        mock_response.raise_for_status = MagicMock()
 
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {"success": True, "agent_json": agent_json}
 
-        instructions = {"type": "instructions", "steps": ["Step 1"]}
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+            instructions = {"type": "instructions", "steps": ["Step 1"]}
             result = await service.generate_agent_external(instructions)
 
         assert result == agent_json
-        mock_client.post.assert_called_once_with(
-            "/api/generate-agent", json={"instructions": instructions}
+        mock_poll.assert_called_once_with(
+            "/api/generate-agent",
+            {"instructions": instructions},
         )
 
     @pytest.mark.asyncio
     async def test_generate_agent_handles_error(self):
         """Test agent generation handles errors gracefully."""
-        mock_client = AsyncMock()
-        mock_client.post.side_effect = httpx.RequestError("Connection failed")
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {
+                "type": "error",
+                "error": "Connection failed",
+                "error_type": "connection_error",
+            }
             result = await service.generate_agent_external({"steps": []})
 
         assert result is not None
         assert result.get("type") == "error"
         assert result.get("error_type") == "connection_error"
-        assert "Connection failed" in result.get("error", "")
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_missing_agent_json(self):
+        """Test that missing agent_json in result returns an error."""
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {"success": True}
+            result = await service.generate_agent_external({"steps": ["Step 1"]})
+
+        assert result is not None
+        assert result.get("type") == "error"
+        assert result.get("error_type") == "invalid_response"
 
 
 class TestGenerateAgentPatchExternal:
@@ -274,27 +552,24 @@ class TestGenerateAgentPatchExternal:
             "nodes": [{"id": "1", "block_id": "test"}],
             "links": [],
         }
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "agent_json": updated_agent,
-        }
-        mock_response.raise_for_status = MagicMock()
 
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {"success": True, "agent_json": updated_agent}
 
-        current_agent = {"name": "Old Agent", "nodes": [], "links": []}
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+            current_agent = {"name": "Old Agent", "nodes": [], "links": []}
             result = await service.generate_agent_patch_external(
                 "Add a new node", current_agent
             )
 
         assert result == updated_agent
-        mock_client.post.assert_called_once_with(
+        mock_poll.assert_called_once_with(
             "/api/update-agent",
-            json={
+            {
                 "update_request": "Add a new node",
                 "current_agent_json": current_agent,
             },
@@ -303,18 +578,16 @@ class TestGenerateAgentPatchExternal:
     @pytest.mark.asyncio
     async def test_generate_patch_returns_clarifying_questions(self):
         """Test patch generation returning clarifying questions."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "type": "clarifying_questions",
-            "questions": ["What type of node?"],
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {
+                "type": "clarifying_questions",
+                "questions": ["What type of node?"],
+            }
             result = await service.generate_agent_patch_external(
                 "Add something", {"nodes": []}
             )
@@ -355,9 +628,12 @@ class TestHealthCheck:
         mock_client = AsyncMock()
         mock_client.get.return_value = mock_response
 
-        with patch.object(service, "is_external_service_configured", return_value=True):
-            with patch.object(service, "_get_client", return_value=mock_client):
-                result = await service.health_check()
+        with (
+            patch.object(service, "is_external_service_configured", return_value=True),
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(service, "_get_client", return_value=mock_client),
+        ):
+            result = await service.health_check()
 
         assert result is True
         mock_client.get.assert_called_once_with("/health")
@@ -375,9 +651,12 @@ class TestHealthCheck:
         mock_client = AsyncMock()
         mock_client.get.return_value = mock_response
 
-        with patch.object(service, "is_external_service_configured", return_value=True):
-            with patch.object(service, "_get_client", return_value=mock_client):
-                result = await service.health_check()
+        with (
+            patch.object(service, "is_external_service_configured", return_value=True),
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(service, "_get_client", return_value=mock_client),
+        ):
+            result = await service.health_check()
 
         assert result is False
 
@@ -387,9 +666,12 @@ class TestHealthCheck:
         mock_client = AsyncMock()
         mock_client.get.side_effect = httpx.RequestError("Connection failed")
 
-        with patch.object(service, "is_external_service_configured", return_value=True):
-            with patch.object(service, "_get_client", return_value=mock_client):
-                result = await service.health_check()
+        with (
+            patch.object(service, "is_external_service_configured", return_value=True),
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(service, "_get_client", return_value=mock_client),
+        ):
+            result = await service.health_check()
 
         assert result is False
 
@@ -419,7 +701,10 @@ class TestGetBlocksExternal:
         mock_client = AsyncMock()
         mock_client.get.return_value = mock_response
 
-        with patch.object(service, "_get_client", return_value=mock_client):
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(service, "_get_client", return_value=mock_client),
+        ):
             result = await service.get_blocks_external()
 
         assert result == blocks
@@ -431,7 +716,10 @@ class TestGetBlocksExternal:
         mock_client = AsyncMock()
         mock_client.get.side_effect = httpx.RequestError("Connection failed")
 
-        with patch.object(service, "_get_client", return_value=mock_client):
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(service, "_get_client", return_value=mock_client),
+        ):
             result = await service.get_blocks_external()
 
         assert result is None
@@ -459,26 +747,22 @@ class TestLibraryAgentsPassthrough:
             },
         ]
 
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "type": "instructions",
-            "steps": ["Step 1"],
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {"type": "instructions", "steps": ["Step 1"]}
             await service.decompose_goal_external(
                 "Send an email",
                 library_agents=library_agents,
             )
 
         # Verify library_agents was passed in the payload
-        call_args = mock_client.post.call_args
-        assert call_args[1]["json"]["library_agents"] == library_agents
+        call_args = mock_poll.call_args
+        payload = call_args[0][1]
+        assert payload["library_agents"] == library_agents
 
     @pytest.mark.asyncio
     async def test_generate_agent_passes_library_agents(self):
@@ -494,25 +778,24 @@ class TestLibraryAgentsPassthrough:
             },
         ]
 
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "agent_json": {"name": "Test Agent", "nodes": []},
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {
+                "agent_json": {"name": "Test Agent", "nodes": []},
+            }
             await service.generate_agent_external(
                 {"steps": ["Step 1"]},
                 library_agents=library_agents,
             )
 
         # Verify library_agents was passed in the payload
-        call_args = mock_client.post.call_args
-        assert call_args[1]["json"]["library_agents"] == library_agents
+        call_args = mock_poll.call_args
+        payload = call_args[0][1]
+        assert payload["library_agents"] == library_agents
 
     @pytest.mark.asyncio
     async def test_generate_agent_patch_passes_library_agents(self):
@@ -528,17 +811,15 @@ class TestLibraryAgentsPassthrough:
             },
         ]
 
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "agent_json": {"name": "Updated Agent", "nodes": []},
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {
+                "agent_json": {"name": "Updated Agent", "nodes": []},
+            }
             await service.generate_agent_patch_external(
                 "Add error handling",
                 {"name": "Original Agent", "nodes": []},
@@ -546,29 +827,26 @@ class TestLibraryAgentsPassthrough:
             )
 
         # Verify library_agents was passed in the payload
-        call_args = mock_client.post.call_args
-        assert call_args[1]["json"]["library_agents"] == library_agents
+        call_args = mock_poll.call_args
+        payload = call_args[0][1]
+        assert payload["library_agents"] == library_agents
 
     @pytest.mark.asyncio
     async def test_decompose_goal_without_library_agents(self):
         """Test that decompose goal works without library_agents."""
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "success": True,
-            "type": "instructions",
-            "steps": ["Step 1"],
-        }
-        mock_response.raise_for_status = MagicMock()
-
-        mock_client = AsyncMock()
-        mock_client.post.return_value = mock_response
-
-        with patch.object(service, "_get_client", return_value=mock_client):
+        with (
+            patch.object(service, "_is_dummy_mode", return_value=False),
+            patch.object(
+                service, "_submit_and_poll", new_callable=AsyncMock
+            ) as mock_poll,
+        ):
+            mock_poll.return_value = {"type": "instructions", "steps": ["Step 1"]}
             await service.decompose_goal_external("Build a workflow")
 
         # Verify library_agents was NOT passed when not provided
-        call_args = mock_client.post.call_args
-        assert "library_agents" not in call_args[1]["json"]
+        call_args = mock_poll.call_args
+        payload = call_args[0][1]
+        assert "library_agents" not in payload
 
 
 if __name__ == "__main__":

autogpt_platform/backend/test_requeue_integration.py

@@ -1,349 +0,0 @@
-#!/usr/bin/env python3
-"""
-Integration test for the requeue fix implementation.
-Tests actual RabbitMQ behavior to verify that republishing sends messages to back of queue.
-"""
-
-import json
-import time
-from threading import Event
-from typing import List
-
-from backend.data.rabbitmq import SyncRabbitMQ
-from backend.executor.utils import create_execution_queue_config
-
-
-class QueueOrderTester:
-    """Helper class to test message ordering in RabbitMQ using a dedicated test queue."""
-
-    def __init__(self):
-        self.received_messages: List[dict] = []
-        self.stop_consuming = Event()
-        self.queue_client = SyncRabbitMQ(create_execution_queue_config())
-        self.queue_client.connect()
-
-        # Use a dedicated test queue name to avoid conflicts
-        self.test_queue_name = "test_requeue_ordering"
-        self.test_exchange = "test_exchange"
-        self.test_routing_key = "test.requeue"
-
-    def setup_queue(self):
-        """Set up a dedicated test queue for testing."""
-        channel = self.queue_client.get_channel()
-
-        # Declare test exchange
-        channel.exchange_declare(
-            exchange=self.test_exchange, exchange_type="direct", durable=True
-        )
-
-        # Declare test queue
-        channel.queue_declare(
-            queue=self.test_queue_name, durable=True, auto_delete=False
-        )
-
-        # Bind queue to exchange
-        channel.queue_bind(
-            exchange=self.test_exchange,
-            queue=self.test_queue_name,
-            routing_key=self.test_routing_key,
-        )
-
-        # Purge the queue to start fresh
-        channel.queue_purge(self.test_queue_name)
-        print(f"✅ Test queue {self.test_queue_name} setup and purged")
-
-    def create_test_message(self, message_id: str, user_id: str = "test-user") -> str:
-        """Create a test graph execution message."""
-        return json.dumps(
-            {
-                "graph_exec_id": f"exec-{message_id}",
-                "graph_id": f"graph-{message_id}",
-                "user_id": user_id,
-                "execution_context": {"timezone": "UTC"},
-                "nodes_input_masks": {},
-                "starting_nodes_input": [],
-            }
-        )
-
-    def publish_message(self, message: str):
-        """Publish a message to the test queue."""
-        channel = self.queue_client.get_channel()
-        channel.basic_publish(
-            exchange=self.test_exchange,
-            routing_key=self.test_routing_key,
-            body=message,
-        )
-
-    def consume_messages(self, max_messages: int = 10, timeout: float = 5.0):
-        """Consume messages and track their order."""
-
-        def callback(ch, method, properties, body):
-            try:
-                message_data = json.loads(body.decode())
-                self.received_messages.append(message_data)
-                ch.basic_ack(delivery_tag=method.delivery_tag)
-
-                if len(self.received_messages) >= max_messages:
-                    self.stop_consuming.set()
-            except Exception as e:
-                print(f"Error processing message: {e}")
-                ch.basic_nack(delivery_tag=method.delivery_tag, requeue=False)
-
-        # Use synchronous consumption with blocking
-        channel = self.queue_client.get_channel()
-
-        # Check if there are messages in the queue first
-        method_frame, header_frame, body = channel.basic_get(
-            queue=self.test_queue_name, auto_ack=False
-        )
-        if method_frame:
-            # There are messages, set up consumer
-            channel.basic_nack(
-                delivery_tag=method_frame.delivery_tag, requeue=True
-            )  # Put message back
-
-            # Set up consumer
-            channel.basic_consume(
-                queue=self.test_queue_name,
-                on_message_callback=callback,
-            )
-
-            # Consume with timeout
-            start_time = time.time()
-            while (
-                not self.stop_consuming.is_set()
-                and (time.time() - start_time) < timeout
-                and len(self.received_messages) < max_messages
-            ):
-                try:
-                    channel.connection.process_data_events(time_limit=0.1)
-                except Exception as e:
-                    print(f"Error during consumption: {e}")
-                    break
-
-            # Cancel the consumer
-            try:
-                channel.cancel()
-            except Exception:
-                pass
-        else:
-            # No messages in queue - this might be expected for some tests
-            pass
-
-        return self.received_messages
-
-    def cleanup(self):
-        """Clean up test resources."""
-        try:
-            channel = self.queue_client.get_channel()
-            channel.queue_delete(queue=self.test_queue_name)
-            channel.exchange_delete(exchange=self.test_exchange)
-            print(f"✅ Test queue {self.test_queue_name} cleaned up")
-        except Exception as e:
-            print(f"⚠️ Cleanup issue: {e}")
-
-
-def test_queue_ordering_behavior():
-    """
-    Integration test to verify that our republishing method sends messages to back of queue.
-    This tests the actual fix for the rate limiting queue blocking issue.
-    """
-    tester = QueueOrderTester()
-
-    try:
-        tester.setup_queue()
-
-        print("🧪 Testing actual RabbitMQ queue ordering behavior...")
-
-        # Test 1: Normal FIFO behavior
-        print("1. Testing normal FIFO queue behavior")
-
-        # Publish messages in order: A, B, C
-        msg_a = tester.create_test_message("A")
-        msg_b = tester.create_test_message("B")
-        msg_c = tester.create_test_message("C")
-
-        tester.publish_message(msg_a)
-        tester.publish_message(msg_b)
-        tester.publish_message(msg_c)
-
-        # Consume and verify FIFO order: A, B, C
-        tester.received_messages = []
-        tester.stop_consuming.clear()
-        messages = tester.consume_messages(max_messages=3)
-
-        assert len(messages) == 3, f"Expected 3 messages, got {len(messages)}"
-        assert (
-            messages[0]["graph_exec_id"] == "exec-A"
-        ), f"First message should be A, got {messages[0]['graph_exec_id']}"
-        assert (
-            messages[1]["graph_exec_id"] == "exec-B"
-        ), f"Second message should be B, got {messages[1]['graph_exec_id']}"
-        assert (
-            messages[2]["graph_exec_id"] == "exec-C"
-        ), f"Third message should be C, got {messages[2]['graph_exec_id']}"
-
-        print("✅ FIFO order confirmed: A -> B -> C")
-
-        # Test 2: Rate limiting simulation - the key test!
-        print("2. Testing rate limiting fix scenario")
-
-        # Simulate the scenario where user1 is rate limited
-        user1_msg = tester.create_test_message("RATE-LIMITED", "user1")
-        user2_msg1 = tester.create_test_message("USER2-1", "user2")
-        user2_msg2 = tester.create_test_message("USER2-2", "user2")
-
-        # Initially publish user1 message (gets consumed, then rate limited on retry)
-        tester.publish_message(user1_msg)
-
-        # Other users publish their messages
-        tester.publish_message(user2_msg1)
-        tester.publish_message(user2_msg2)
-
-        # Now simulate: user1 message gets "requeued" using our new republishing method
-        # This is what happens in manager.py when requeue_by_republishing=True
-        tester.publish_message(user1_msg)  # Goes to back via our method
-
-        # Expected order: RATE-LIMITED, USER2-1, USER2-2, RATE-LIMITED (republished to back)
-        # This shows that user2 messages get processed instead of being blocked
-        tester.received_messages = []
-        tester.stop_consuming.clear()
-        messages = tester.consume_messages(max_messages=4)
-
-        assert len(messages) == 4, f"Expected 4 messages, got {len(messages)}"
-
-        # The key verification: user2 messages are NOT blocked by user1's rate-limited message
-        user2_messages = [msg for msg in messages if msg["user_id"] == "user2"]
-        assert len(user2_messages) == 2, "Both user2 messages should be processed"
-        assert user2_messages[0]["graph_exec_id"] == "exec-USER2-1"
-        assert user2_messages[1]["graph_exec_id"] == "exec-USER2-2"
-
-        print("✅ Rate limiting fix confirmed: user2 executions NOT blocked by user1")
-
-        # Test 3: Verify our method behaves like going to back of queue
-        print("3. Testing republishing sends messages to back")
-
-        # Start with message X in queue
-        msg_x = tester.create_test_message("X")
-        tester.publish_message(msg_x)
-
-        # Add message Y
-        msg_y = tester.create_test_message("Y")
-        tester.publish_message(msg_y)
-
-        # Republish X (simulates requeue using our method)
-        tester.publish_message(msg_x)
-
-        # Expected: X, Y, X (X was republished to back)
-        tester.received_messages = []
-        tester.stop_consuming.clear()
-        messages = tester.consume_messages(max_messages=3)
-
-        assert len(messages) == 3
-        # Y should come before the republished X
-        y_index = next(
-            i for i, msg in enumerate(messages) if msg["graph_exec_id"] == "exec-Y"
-        )
-        republished_x_index = next(
-            i
-            for i, msg in enumerate(messages[1:], 1)
-            if msg["graph_exec_id"] == "exec-X"
-        )
-
-        assert (
-            y_index < republished_x_index
-        ), f"Y should come before republished X, but got order: {[m['graph_exec_id'] for m in messages]}"
-
-        print("✅ Republishing confirmed: messages go to back of queue")
-
-        print("🎉 All integration tests passed!")
-        print("🎉 Our republishing method works correctly with real RabbitMQ")
-        print("🎉 Queue blocking issue is fixed!")
-
-    finally:
-        tester.cleanup()
-
-
-def test_traditional_requeue_behavior():
-    """
-    Test that traditional requeue (basic_nack with requeue=True) sends messages to FRONT of queue.
-    This validates our hypothesis about why queue blocking occurs.
-    """
-    tester = QueueOrderTester()
-
-    try:
-        tester.setup_queue()
-        print("🧪 Testing traditional requeue behavior (basic_nack with requeue=True)")
-
-        # Step 1: Publish message A
-        msg_a = tester.create_test_message("A")
-        tester.publish_message(msg_a)
-
-        # Step 2: Publish message B
-        msg_b = tester.create_test_message("B")
-        tester.publish_message(msg_b)
-
-        # Step 3: Consume message A and requeue it using traditional method
-        channel = tester.queue_client.get_channel()
-        method_frame, header_frame, body = channel.basic_get(
-            queue=tester.test_queue_name, auto_ack=False
-        )
-
-        assert method_frame is not None, "Should have received message A"
-        consumed_msg = json.loads(body.decode())
-        assert (
-            consumed_msg["graph_exec_id"] == "exec-A"
-        ), f"Should have consumed message A, got {consumed_msg['graph_exec_id']}"
-
-        # Traditional requeue: basic_nack with requeue=True (sends to FRONT)
-        channel.basic_nack(delivery_tag=method_frame.delivery_tag, requeue=True)
-        print(f"🔄 Traditional requeue (to FRONT): {consumed_msg['graph_exec_id']}")
-
-        # Step 4: Consume all messages using basic_get for reliability
-        received_messages = []
-
-        # Get first message
-        method_frame, header_frame, body = channel.basic_get(
-            queue=tester.test_queue_name, auto_ack=True
-        )
-        if method_frame:
-            msg = json.loads(body.decode())
-            received_messages.append(msg)
-
-        # Get second message
-        method_frame, header_frame, body = channel.basic_get(
-            queue=tester.test_queue_name, auto_ack=True
-        )
-        if method_frame:
-            msg = json.loads(body.decode())
-            received_messages.append(msg)
-
-        # CRITICAL ASSERTION: Traditional requeue should put A at FRONT
-        # Expected order: A (requeued to front), B
-        assert (
-            len(received_messages) == 2
-        ), f"Expected 2 messages, got {len(received_messages)}"
-
-        first_msg = received_messages[0]["graph_exec_id"]
-        second_msg = received_messages[1]["graph_exec_id"]
-
-        # This is the critical test: requeued message A should come BEFORE B
-        assert (
-            first_msg == "exec-A"
-        ), f"Traditional requeue should put A at FRONT, but first message was: {first_msg}"
-        assert (
-            second_msg == "exec-B"
-        ), f"B should come after requeued A, but second message was: {second_msg}"
-
-        print(
-            "✅ HYPOTHESIS CONFIRMED: Traditional requeue sends messages to FRONT of queue"
-        )
-        print(f"   Order: {first_msg} (requeued to front) → {second_msg}")
-        print("   This explains why rate-limited messages block other users!")
-
-    finally:
-        tester.cleanup()
-
-
-if __name__ == "__main__":
-    test_queue_ordering_behavior()

autogpt_platform/frontend/src/app/(platform)/copilot/CopilotPage.tsx

@@ -27,6 +27,7 @@ export function CopilotPage() {
     createSession,
     onSend,
     isLoadingSession,
+    isSessionError,
     isCreatingSession,
     isUserLoading,
     isLoggedIn,
@@ -71,6 +72,7 @@ export function CopilotPage() {
             error={error}
             sessionId={sessionId}
             isLoadingSession={isLoadingSession}
+            isSessionError={isSessionError}
             isCreatingSession={isCreatingSession}
             isReconnecting={isReconnecting}
             onCreateSession={createSession}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatContainer/ChatContainer.tsx

@@ -13,6 +13,7 @@ export interface ChatContainerProps {
   error: Error | undefined;
   sessionId: string | null;
   isLoadingSession: boolean;
+  isSessionError?: boolean;
   isCreatingSession: boolean;
   /** True when backend has an active stream but we haven't reconnected yet. */
   isReconnecting?: boolean;
@@ -27,6 +28,7 @@ export const ChatContainer = ({
   error,
   sessionId,
   isLoadingSession,
+  isSessionError,
   isCreatingSession,
   isReconnecting,
   onCreateSession,
@@ -34,7 +36,12 @@ export const ChatContainer = ({
   onStop,
   headerSlot,
 }: ChatContainerProps) => {
-  const isBusy = status === "streaming" || !!isReconnecting;
+  const isBusy =
+    status === "streaming" ||
+    status === "submitted" ||
+    !!isReconnecting ||
+    isLoadingSession ||
+    !!isSessionError;
   const inputLayoutId = "copilot-2-chat-input";
 
   return (

autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatMessagesContainer/ChatMessagesContainer.tsx

@@ -10,9 +10,8 @@ import {
   MessageResponse,
 } from "@/components/ai-elements/message";
 import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { toast } from "@/components/molecules/Toast/use-toast";
 import { ToolUIPart, UIDataTypes, UIMessage, UITools } from "ai";
-import { useEffect, useRef, useState } from "react";
+import { useEffect, useState } from "react";
 import { CreateAgentTool } from "../../tools/CreateAgent/CreateAgent";
 import { EditAgentTool } from "../../tools/EditAgent/EditAgent";
 import {
@@ -129,7 +128,6 @@ export const ChatMessagesContainer = ({
   headerSlot,
 }: ChatMessagesContainerProps) => {
   const [thinkingPhrase, setThinkingPhrase] = useState(getRandomPhrase);
-  const lastToastTimeRef = useRef(0);
 
   useEffect(() => {
     if (status === "submitted") {
@@ -137,20 +135,6 @@ export const ChatMessagesContainer = ({
     }
   }, [status]);
 
-  // Show a toast when a new error occurs, debounced to avoid spam
-  useEffect(() => {
-    if (!error) return;
-    const now = Date.now();
-    if (now - lastToastTimeRef.current < 3_000) return;
-    lastToastTimeRef.current = now;
-    toast({
-      variant: "destructive",
-      title: "Something went wrong",
-      description:
-        "The assistant encountered an error. Please try sending your message again.",
-    });
-  }, [error]);
-
   const lastMessage = messages[messages.length - 1];
   const lastAssistantHasVisibleContent =
     lastMessage?.role === "assistant" &&
@@ -314,13 +298,15 @@ export const ChatMessagesContainer = ({
           </Message>
         )}
         {error && (
-          <div className="rounded-lg bg-red-50 p-4 text-sm text-red-700">
-            <p className="font-medium">Something went wrong</p>
-            <p className="mt-1 text-red-600">
+          <details className="rounded-lg bg-red-50 p-4 text-sm text-red-700">
+            <summary className="cursor-pointer font-medium">
               The assistant encountered an error. Please try sending your
               message again.
-            </p>
-          </div>
+            </summary>
+            <pre className="mt-2 max-h-40 overflow-auto whitespace-pre-wrap break-words text-xs text-red-600">
+              {error instanceof Error ? error.message : String(error)}
+            </pre>
+          </details>
         )}
       </ConversationContent>
       <ConversationScrollButton />

autogpt_platform/frontend/src/app/(platform)/copilot/helpers/convertChatSessionToUiMessages.ts

@@ -116,12 +116,10 @@ export function convertChatSessionMessagesToUiMessages(
             output: "",
           });
         } else {
-          parts.push({
-            type: `tool-${toolName}`,
-            toolCallId,
-            state: "input-available",
-            input,
-          });
+          // Active stream exists: Skip incomplete tool calls during hydration.
+          // The resume stream will deliver them fresh with proper SDK state.
+          // This prevents "No tool invocation found" errors on page refresh.
+          continue;
         }
       }
     }

autogpt_platform/frontend/src/app/(platform)/copilot/hooks/useAsymptoticProgress.ts

@@ -1,47 +0,0 @@
-import { useEffect, useRef, useState } from "react";
-
-/**
- * Hook that returns a progress value that starts fast and slows down,
- * asymptotically approaching but never reaching the max value.
- *
- * Uses a half-life formula: progress = max * (1 - 0.5^(time/halfLife))
- * This creates a "loading bar" effect where:
- * - 50% is reached at halfLifeSeconds
- * - 75% is reached at 2 * halfLifeSeconds
- * - 87.5% is reached at 3 * halfLifeSeconds
- *
- * @param isActive - Whether the progress should be animating
- * @param halfLifeSeconds - Time in seconds to reach 50% progress (default: 30)
- * @param maxProgress - Maximum progress value to approach (default: 100)
- * @param intervalMs - Update interval in milliseconds (default: 100)
- * @returns Current progress value (0–maxProgress)
- */
-export function useAsymptoticProgress(
-  isActive: boolean,
-  halfLifeSeconds = 30,
-  maxProgress = 100,
-  intervalMs = 100,
-) {
-  const [progress, setProgress] = useState(0);
-  const elapsedTimeRef = useRef(0);
-
-  useEffect(() => {
-    if (!isActive) {
-      setProgress(0);
-      elapsedTimeRef.current = 0;
-      return;
-    }
-
-    const interval = setInterval(() => {
-      elapsedTimeRef.current += intervalMs / 1000;
-      const newProgress =
-        maxProgress *
-        (1 - Math.pow(0.5, elapsedTimeRef.current / halfLifeSeconds));
-      setProgress(newProgress);
-    }, intervalMs);
-
-    return () => clearInterval(interval);
-  }, [isActive, halfLifeSeconds, maxProgress, intervalMs]);
-
-  return progress;
-}

autogpt_platform/frontend/src/app/(platform)/copilot/hooks/useLongRunningToolPolling.ts

@@ -1,126 +0,0 @@
-import { getGetV2GetSessionQueryKey } from "@/app/api/__generated__/endpoints/chat/chat";
-import { useQueryClient } from "@tanstack/react-query";
-import type { UIDataTypes, UIMessage, UITools } from "ai";
-import { useCallback, useEffect, useRef } from "react";
-import { convertChatSessionMessagesToUiMessages } from "../helpers/convertChatSessionToUiMessages";
-
-const OPERATING_TYPES = new Set([
-  "operation_started",
-  "operation_pending",
-  "operation_in_progress",
-]);
-
-const POLL_INTERVAL_MS = 1_500;
-
-/**
- * Detects whether any message contains a tool part whose output indicates
- * a long-running operation is still in progress.
- */
-function hasOperatingTool(
-  messages: UIMessage<unknown, UIDataTypes, UITools>[],
-) {
-  for (const msg of messages) {
-    for (const part of msg.parts) {
-      if (!part.type.startsWith("tool-")) continue;
-      const toolPart = part as { output?: unknown };
-      if (!toolPart.output) continue;
-      const output =
-        typeof toolPart.output === "string"
-          ? safeParse(toolPart.output)
-          : toolPart.output;
-      if (
-        output &&
-        typeof output === "object" &&
-        "type" in output &&
-        OPERATING_TYPES.has((output as { type: string }).type)
-      ) {
-        return true;
-      }
-    }
-  }
-  return false;
-}
-
-function safeParse(value: string): unknown {
-  try {
-    return JSON.parse(value);
-  } catch {
-    return null;
-  }
-}
-
-/**
- * Polls the session endpoint while any tool is in an "operating" state
- * (operation_started / operation_pending / operation_in_progress).
- *
- * When the session data shows the tool output has changed (e.g. to
- * agent_saved), it calls `setMessages` with the updated messages.
- */
-export function useLongRunningToolPolling(
-  sessionId: string | null,
-  messages: UIMessage<unknown, UIDataTypes, UITools>[],
-  setMessages: (
-    updater: (
-      prev: UIMessage<unknown, UIDataTypes, UITools>[],
-    ) => UIMessage<unknown, UIDataTypes, UITools>[],
-  ) => void,
-) {
-  const queryClient = useQueryClient();
-  const intervalRef = useRef<ReturnType<typeof setInterval> | null>(null);
-
-  const stopPolling = useCallback(() => {
-    if (intervalRef.current) {
-      clearInterval(intervalRef.current);
-      intervalRef.current = null;
-    }
-  }, []);
-
-  const poll = useCallback(async () => {
-    if (!sessionId) return;
-
-    // Invalidate the query cache so the next fetch gets fresh data
-    await queryClient.invalidateQueries({
-      queryKey: getGetV2GetSessionQueryKey(sessionId),
-    });
-
-    // Fetch fresh session data
-    const data = queryClient.getQueryData<{
-      status: number;
-      data: { messages?: unknown[] };
-    }>(getGetV2GetSessionQueryKey(sessionId));
-
-    if (data?.status !== 200 || !data.data.messages) return;
-
-    const freshMessages = convertChatSessionMessagesToUiMessages(
-      sessionId,
-      data.data.messages,
-    );
-
-    if (!freshMessages || freshMessages.length === 0) return;
-
-    // Update when the long-running tool completed
-    if (!hasOperatingTool(freshMessages)) {
-      setMessages(() => freshMessages);
-      stopPolling();
-    }
-  }, [sessionId, queryClient, setMessages, stopPolling]);
-
-  useEffect(() => {
-    const shouldPoll = hasOperatingTool(messages);
-
-    // Always clear any previous interval first so we never leak timers
-    // when the effect re-runs due to dependency changes (e.g. messages
-    // updating as the LLM streams text after the tool call).
-    stopPolling();
-
-    if (shouldPoll && sessionId) {
-      intervalRef.current = setInterval(() => {
-        poll();
-      }, POLL_INTERVAL_MS);
-    }
-
-    return () => {
-      stopPolling();
-    };
-  }, [messages, sessionId, poll, stopPolling]);
-}

autogpt_platform/frontend/src/app/(platform)/copilot/styleguide/page.tsx

@@ -1120,56 +1120,6 @@ export default function StyleguidePage() {
                 />
               </SubSection>
 
-              <SubSection label="Output available (operation started)">
-                <CreateAgentTool
-                  part={{
-                    type: "tool-create_agent",
-                    toolCallId: uid(),
-                    state: "output-available",
-                    output: {
-                      type: ResponseType.operation_started,
-                      operation_id: "op-create-123",
-                      tool_name: "create_agent",
-                      message:
-                        "Agent creation has been started. This may take a moment.",
-                    },
-                  }}
-                />
-              </SubSection>
-
-              <SubSection label="Output available (operation pending)">
-                <CreateAgentTool
-                  part={{
-                    type: "tool-create_agent",
-                    toolCallId: uid(),
-                    state: "output-available",
-                    output: {
-                      type: ResponseType.operation_pending,
-                      operation_id: "op-create-123",
-                      tool_name: "create_agent",
-                      message:
-                        "Agent creation is queued and will begin shortly.",
-                    },
-                  }}
-                />
-              </SubSection>
-
-              <SubSection label="Output available (operation in progress)">
-                <CreateAgentTool
-                  part={{
-                    type: "tool-create_agent",
-                    toolCallId: uid(),
-                    state: "output-available",
-                    output: {
-                      type: ResponseType.operation_in_progress,
-                      tool_call_id: "tc-456",
-                      message:
-                        "An agent creation operation is already in progress. Please wait for it to finish.",
-                    },
-                  }}
-                />
-              </SubSection>
-
               <SubSection label="Output available (agent preview)">
                 <CreateAgentTool
                   part={{
@@ -1292,22 +1242,6 @@ export default function StyleguidePage() {
                 />
               </SubSection>
 
-              <SubSection label="Output available (operation started)">
-                <EditAgentTool
-                  part={{
-                    type: "tool-edit_agent",
-                    toolCallId: uid(),
-                    state: "output-available",
-                    output: {
-                      type: ResponseType.operation_started,
-                      operation_id: "op-edit-456",
-                      tool_name: "edit_agent",
-                      message: "Agent editing has started.",
-                    },
-                  }}
-                />
-              </SubSection>
-
               <SubSection label="Output available (agent preview)">
                 <EditAgentTool
                   part={{

autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/CreateAgent.tsx

@@ -16,6 +16,7 @@ import {
   ContentCardDescription,
   ContentCodeBlock,
   ContentGrid,
+  ContentHint,
   ContentMessage,
 } from "../../components/ToolAccordion/AccordionContent";
 import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
@@ -35,9 +36,6 @@ import {
   isAgentSavedOutput,
   isClarificationNeededOutput,
   isErrorOutput,
-  isOperationInProgressOutput,
-  isOperationPendingOutput,
-  isOperationStartedOutput,
   isSuggestedGoalOutput,
   ToolIcon,
   truncateText,
@@ -56,9 +54,18 @@ interface Props {
   part: CreateAgentToolPart;
 }
 
-function getAccordionMeta(output: CreateAgentToolOutput) {
+function getAccordionMeta(output: CreateAgentToolOutput | null) {
   const icon = <AccordionIcon />;
 
+  if (!output) {
+    return {
+      icon,
+      title:
+        "Creating agent, this may take a few minutes. Play while you wait.",
+      expanded: true,
+    };
+  }
+
   if (isAgentSavedOutput(output)) {
     return { icon, title: output.agent_name, expanded: true };
   }
@@ -85,16 +92,6 @@ function getAccordionMeta(output: CreateAgentToolOutput) {
       expanded: true,
     };
   }
-  if (
-    isOperationStartedOutput(output) ||
-    isOperationPendingOutput(output) ||
-    isOperationInProgressOutput(output)
-  ) {
-    return {
-      icon,
-      title: output.message || "Agent creation started",
-    };
-  }
   return {
     icon: (
       <WarningDiamondIcon size={32} weight="light" className="text-red-500" />
@@ -116,23 +113,11 @@ export function CreateAgentTool({ part }: Props) {
   const isError =
     part.state === "output-error" || (!!output && isErrorOutput(output));
 
-  const isOperating =
-    !!output &&
-    (isOperationStartedOutput(output) ||
-      isOperationPendingOutput(output) ||
-      isOperationInProgressOutput(output));
+  const isOperating = !output;
 
-  const hasExpandableContent =
-    part.state === "output-available" &&
-    !!output &&
-    (isOperationStartedOutput(output) ||
-      isOperationPendingOutput(output) ||
-      isOperationInProgressOutput(output) ||
-      isAgentPreviewOutput(output) ||
-      isAgentSavedOutput(output) ||
-      isClarificationNeededOutput(output) ||
-      isSuggestedGoalOutput(output) ||
-      isErrorOutput(output));
+  // Show accordion for operating state and successful outputs, but not for errors
+  // (errors are shown inline so they get replaced when retrying)
+  const hasExpandableContent = !isError;
 
   function handleUseSuggestedGoal(goal: string) {
     onSend(`Please create an agent with this goal: ${goal}`);
@@ -158,33 +143,77 @@ export function CreateAgentTool({ part }: Props) {
 
   return (
     <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {isStreaming && (
-        <ToolAccordion
-          icon={<AccordionIcon />}
-          title="Creating agent, this may take a few minutes. Play while you wait."
-          expanded
-        >
-          <ContentGrid>
-            <MiniGame />
-          </ContentGrid>
-        </ToolAccordion>
+      {isOperating && (
+        <div className="flex items-center gap-2 text-sm text-muted-foreground">
+          <ToolIcon isStreaming={isStreaming} isError={isError} />
+          <MorphingTextAnimation
+            text={text}
+            className={isError ? "text-red-500" : undefined}
+          />
+        </div>
       )}
 
-      {hasExpandableContent && output && (
+      {isError && output && isErrorOutput(output) && (
+        <div className="space-y-3 rounded-lg border border-red-200 bg-red-50 p-4">
+          <div className="flex items-start gap-2">
+            <WarningDiamondIcon
+              size={20}
+              weight="regular"
+              className="mt-0.5 shrink-0 text-red-500"
+            />
+            <div className="flex-1 space-y-2">
+              <Text variant="body-medium" className="text-red-900">
+                {output.message ||
+                  "Failed to generate the agent. Please try again."}
+              </Text>
+              {output.error && (
+                <details className="text-xs text-red-700">
+                  <summary className="cursor-pointer font-medium">
+                    Technical details
+                  </summary>
+                  <pre className="mt-2 max-h-40 overflow-auto whitespace-pre-wrap break-words rounded bg-red-100 p-2">
+                    {formatMaybeJson(output.error)}
+                  </pre>
+                </details>
+              )}
+              {output.details && (
+                <pre className="max-h-40 overflow-auto whitespace-pre-wrap break-words rounded bg-red-100 p-2 text-xs text-red-700">
+                  {formatMaybeJson(output.details)}
+                </pre>
+              )}
+            </div>
+          </div>
+          <div className="flex gap-2">
+            <Button
+              variant="outline"
+              size="small"
+              onClick={() => onSend("Please try creating the agent again.")}
+            >
+              Try again
+            </Button>
+            <Button
+              variant="outline"
+              size="small"
+              onClick={() => onSend("Can you help me simplify this goal?")}
+            >
+              Simplify goal
+            </Button>
+          </div>
+        </div>
+      )}
+
+      {hasExpandableContent && (
         <ToolAccordion {...getAccordionMeta(output)}>
-          {isOperating && output.message && (
-            <ContentMessage>{output.message}</ContentMessage>
+          {isOperating && (
+            <ContentGrid>
+              <MiniGame />
+              <ContentHint>
+                This could take a few minutes — play while you wait!
+              </ContentHint>
+            </ContentGrid>
           )}
 
-          {isAgentSavedOutput(output) && (
+          {output && isAgentSavedOutput(output) && (
             <div className="rounded-xl border border-border/60 bg-card p-4 shadow-sm">
               <div className="flex items-baseline gap-2">
                 <Image
@@ -230,7 +259,7 @@ export function CreateAgentTool({ part }: Props) {
             </div>
           )}
 
-          {isAgentPreviewOutput(output) && (
+          {output && isAgentPreviewOutput(output) && (
             <ContentGrid>
               <ContentMessage>{output.message}</ContentMessage>
               {output.description?.trim() && (
@@ -244,7 +273,7 @@ export function CreateAgentTool({ part }: Props) {
             </ContentGrid>
           )}
 
-          {isClarificationNeededOutput(output) && (
+          {output && isClarificationNeededOutput(output) && (
             <ClarificationQuestionsCard
               questions={(output.questions ?? []).map((q) => {
                 const item: ClarifyingQuestion = {
@@ -263,7 +292,7 @@ export function CreateAgentTool({ part }: Props) {
             />
           )}
 
-          {isSuggestedGoalOutput(output) && (
+          {output && isSuggestedGoalOutput(output) && (
             <SuggestedGoalCard
               message={output.message}
               suggestedGoal={output.suggested_goal}
@@ -272,38 +301,6 @@ export function CreateAgentTool({ part }: Props) {
               onUseSuggestedGoal={handleUseSuggestedGoal}
             />
           )}
-
-          {isErrorOutput(output) && (
-            <ContentGrid>
-              <ContentMessage>{output.message}</ContentMessage>
-              {output.error && (
-                <ContentCodeBlock>
-                  {formatMaybeJson(output.error)}
-                </ContentCodeBlock>
-              )}
-              {output.details && (
-                <ContentCodeBlock>
-                  {formatMaybeJson(output.details)}
-                </ContentCodeBlock>
-              )}
-              <div className="flex gap-2">
-                <Button
-                  variant="outline"
-                  size="small"
-                  onClick={() => onSend("Please try creating the agent again.")}
-                >
-                  Try again
-                </Button>
-                <Button
-                  variant="outline"
-                  size="small"
-                  onClick={() => onSend("Can you help me simplify this goal?")}
-                >
-                  Simplify goal
-                </Button>
-              </div>
-            </ContentGrid>
-          )}
         </ToolAccordion>
       )}
     </div>

autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/helpers.tsx

@@ -2,9 +2,6 @@ import type { AgentPreviewResponse } from "@/app/api/__generated__/models/agentP
 import type { AgentSavedResponse } from "@/app/api/__generated__/models/agentSavedResponse";
 import type { ClarificationNeededResponse } from "@/app/api/__generated__/models/clarificationNeededResponse";
 import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import type { OperationInProgressResponse } from "@/app/api/__generated__/models/operationInProgressResponse";
-import type { OperationPendingResponse } from "@/app/api/__generated__/models/operationPendingResponse";
-import type { OperationStartedResponse } from "@/app/api/__generated__/models/operationStartedResponse";
 import { ResponseType } from "@/app/api/__generated__/models/responseType";
 import type { SuggestedGoalResponse } from "@/app/api/__generated__/models/suggestedGoalResponse";
 import {
@@ -16,9 +13,6 @@ import type { ToolUIPart } from "ai";
 import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
 
 export type CreateAgentToolOutput =
-  | OperationStartedResponse
-  | OperationPendingResponse
-  | OperationInProgressResponse
   | AgentPreviewResponse
   | AgentSavedResponse
   | ClarificationNeededResponse
@@ -39,9 +33,6 @@ function parseOutput(output: unknown): CreateAgentToolOutput | null {
   if (typeof output === "object") {
     const type = (output as { type?: unknown }).type;
     if (
-      type === ResponseType.operation_started ||
-      type === ResponseType.operation_pending ||
-      type === ResponseType.operation_in_progress ||
       type === ResponseType.agent_preview ||
       type === ResponseType.agent_saved ||
       type === ResponseType.clarification_needed ||
@@ -50,9 +41,6 @@ function parseOutput(output: unknown): CreateAgentToolOutput | null {
     ) {
       return output as CreateAgentToolOutput;
     }
-    if ("operation_id" in output && "tool_name" in output)
-      return output as OperationStartedResponse | OperationPendingResponse;
-    if ("tool_call_id" in output) return output as OperationInProgressResponse;
     if ("agent_json" in output && "agent_name" in output)
       return output as AgentPreviewResponse;
     if ("agent_id" in output && "library_agent_id" in output)
@@ -72,30 +60,6 @@ export function getCreateAgentToolOutput(
   return parseOutput((part as { output?: unknown }).output);
 }
 
-export function isOperationStartedOutput(
-  output: CreateAgentToolOutput,
-): output is OperationStartedResponse {
-  return (
-    output.type === ResponseType.operation_started ||
-    ("operation_id" in output && "tool_name" in output)
-  );
-}
-
-export function isOperationPendingOutput(
-  output: CreateAgentToolOutput,
-): output is OperationPendingResponse {
-  return output.type === ResponseType.operation_pending;
-}
-
-export function isOperationInProgressOutput(
-  output: CreateAgentToolOutput,
-): output is OperationInProgressResponse {
-  return (
-    output.type === ResponseType.operation_in_progress ||
-    "tool_call_id" in output
-  );
-}
-
 export function isAgentPreviewOutput(
   output: CreateAgentToolOutput,
 ): output is AgentPreviewResponse {
@@ -144,10 +108,6 @@ export function getAnimationText(part: {
     case "output-available": {
       const output = parseOutput(part.output);
       if (!output) return "Creating a new agent";
-      if (isOperationStartedOutput(output)) return "Agent creation started";
-      if (isOperationPendingOutput(output)) return "Agent creation in progress";
-      if (isOperationInProgressOutput(output))
-        return "Agent creation already in progress";
       if (isAgentSavedOutput(output)) return `Saved ${output.agent_name}`;
       if (isAgentPreviewOutput(output)) return `Preview "${output.agent_name}"`;
       if (isClarificationNeededOutput(output)) return "Needs clarification";

autogpt_platform/frontend/src/app/(platform)/copilot/tools/EditAgent/EditAgent.tsx

@@ -1,18 +1,27 @@
 "use client";
 
-import { WarningDiamondIcon } from "@phosphor-icons/react";
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import {
+  BookOpenIcon,
+  PencilSimpleIcon,
+  WarningDiamondIcon,
+} from "@phosphor-icons/react";
 import type { ToolUIPart } from "ai";
+import Image from "next/image";
+import NextLink from "next/link";
 import { useCopilotChatActions } from "../../components/CopilotChatActionsProvider/useCopilotChatActions";
+import sparklesImg from "../../components/MiniGame/assets/sparkles.png";
+import { MiniGame } from "../../components/MiniGame/MiniGame";
 import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
 import {
   ContentCardDescription,
   ContentCodeBlock,
   ContentGrid,
-  ContentLink,
+  ContentHint,
   ContentMessage,
 } from "../../components/ToolAccordion/AccordionContent";
 import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import { MiniGame } from "../../components/MiniGame/MiniGame";
 import {
   ClarificationQuestionsCard,
   ClarifyingQuestion,
@@ -26,9 +35,6 @@ import {
   isAgentSavedOutput,
   isClarificationNeededOutput,
   isErrorOutput,
-  isOperationInProgressOutput,
-  isOperationPendingOutput,
-  isOperationStartedOutput,
   ToolIcon,
   truncateText,
   type EditAgentToolOutput,
@@ -46,7 +52,7 @@ interface Props {
   part: EditAgentToolPart;
 }
 
-function getAccordionMeta(output: EditAgentToolOutput): {
+function getAccordionMeta(output: EditAgentToolOutput | null): {
   icon: React.ReactNode;
   title: string;
   titleClassName?: string;
@@ -55,8 +61,16 @@ function getAccordionMeta(output: EditAgentToolOutput): {
 } {
   const icon = <AccordionIcon />;
 
+  if (!output) {
+    return {
+      icon,
+      title: "Editing agent, this may take a few minutes. Play while you wait.",
+      expanded: true,
+    };
+  }
+
   if (isAgentSavedOutput(output)) {
-    return { icon, title: output.agent_name };
+    return { icon, title: output.agent_name, expanded: true };
   }
   if (isAgentPreviewOutput(output)) {
     return {
@@ -73,16 +87,6 @@ function getAccordionMeta(output: EditAgentToolOutput): {
       description: `${questions.length} question${questions.length === 1 ? "" : "s"}`,
     };
   }
-  if (
-    isOperationStartedOutput(output) ||
-    isOperationPendingOutput(output) ||
-    isOperationInProgressOutput(output)
-  ) {
-    return {
-      icon,
-      title: output.message || "Agent editing started",
-    };
-  }
   return {
     icon: (
       <WarningDiamondIcon size={32} weight="light" className="text-red-500" />
@@ -101,21 +105,12 @@ export function EditAgentTool({ part }: Props) {
   const output = getEditAgentToolOutput(part);
   const isError =
     part.state === "output-error" || (!!output && isErrorOutput(output));
-  const isOperating =
-    !!output &&
-    (isOperationStartedOutput(output) ||
-      isOperationPendingOutput(output) ||
-      isOperationInProgressOutput(output));
-  const hasExpandableContent =
-    part.state === "output-available" &&
-    !!output &&
-    (isOperationStartedOutput(output) ||
-      isOperationPendingOutput(output) ||
-      isOperationInProgressOutput(output) ||
-      isAgentPreviewOutput(output) ||
-      isAgentSavedOutput(output) ||
-      isClarificationNeededOutput(output) ||
-      isErrorOutput(output));
+
+  const isOperating = !output;
+
+  // Show accordion for operating state and successful outputs, but not for errors
+  // (errors are shown inline so they get replaced when retrying)
+  const hasExpandableContent = !isError;
 
   function handleClarificationAnswers(answers: Record<string, string>) {
     const questions =
@@ -137,53 +132,114 @@ export function EditAgentTool({ part }: Props) {
 
   return (
     <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {isStreaming && (
-        <ToolAccordion
-          icon={<AccordionIcon />}
-          title="Editing agent, this may take a few minutes. Play while you wait."
-          expanded
-        >
-          <ContentGrid>
-            <MiniGame />
-          </ContentGrid>
-        </ToolAccordion>
+      {isOperating && (
+        <div className="flex items-center gap-2 text-sm text-muted-foreground">
+          <ToolIcon isStreaming={isStreaming} isError={isError} />
+          <MorphingTextAnimation
+            text={text}
+            className={isError ? "text-red-500" : undefined}
+          />
+        </div>
       )}
 
-      {hasExpandableContent && output && (
-        <ToolAccordion {...getAccordionMeta(output)}>
-          {isOperating && output.message && (
-            <ContentMessage>{output.message}</ContentMessage>
-          )}
+      {isError && output && isErrorOutput(output) && (
+        <div className="space-y-3 rounded-lg border border-red-200 bg-red-50 p-4">
+          <div className="flex items-start gap-2">
+            <WarningDiamondIcon
+              size={20}
+              weight="regular"
+              className="mt-0.5 shrink-0 text-red-500"
+            />
+            <div className="flex-1 space-y-2">
+              <Text variant="body-medium" className="text-red-900">
+                {output.message ||
+                  "Failed to edit the agent. Please try again."}
+              </Text>
+              {output.error && (
+                <details className="text-xs text-red-700">
+                  <summary className="cursor-pointer font-medium">
+                    Technical details
+                  </summary>
+                  <pre className="mt-2 max-h-40 overflow-auto whitespace-pre-wrap break-words rounded bg-red-100 p-2">
+                    {formatMaybeJson(output.error)}
+                  </pre>
+                </details>
+              )}
+              {output.details && (
+                <pre className="max-h-40 overflow-auto whitespace-pre-wrap break-words rounded bg-red-100 p-2 text-xs text-red-700">
+                  {formatMaybeJson(output.details)}
+                </pre>
+              )}
+            </div>
+          </div>
+          <Button
+            variant="outline"
+            size="small"
+            onClick={() => onSend("Please try editing the agent again.")}
+          >
+            Try again
+          </Button>
+        </div>
+      )}
 
-          {isAgentSavedOutput(output) && (
+      {hasExpandableContent && (
+        <ToolAccordion {...getAccordionMeta(output)}>
+          {isOperating && (
             <ContentGrid>
-              <ContentMessage>{output.message}</ContentMessage>
-              <div className="flex flex-wrap gap-2">
-                <ContentLink href={output.library_agent_link}>
-                  Open in library
-                </ContentLink>
-                <ContentLink href={output.agent_page_link}>
-                  Open in builder
-                </ContentLink>
-              </div>
-              <ContentCodeBlock>
-                {truncateText(
-                  formatMaybeJson({ agent_id: output.agent_id }),
-                  800,
-                )}
-              </ContentCodeBlock>
+              <MiniGame />
+              <ContentHint>
+                This could take a few minutes — play while you wait!
+              </ContentHint>
             </ContentGrid>
           )}
 
-          {isAgentPreviewOutput(output) && (
+          {output && isAgentSavedOutput(output) && (
+            <div className="rounded-xl border border-border/60 bg-card p-4 shadow-sm">
+              <div className="flex items-baseline gap-2">
+                <Image
+                  src={sparklesImg}
+                  alt="sparkles"
+                  width={24}
+                  height={24}
+                  className="relative top-1"
+                />
+                <Text
+                  variant="body-medium"
+                  className="mb-2 text-[16px] text-black"
+                >
+                  Agent{" "}
+                  <span className="text-violet-600">{output.agent_name}</span>{" "}
+                  has been updated!
+                </Text>
+              </div>
+              <div className="mt-3 flex flex-wrap gap-4">
+                <Button variant="outline" size="small">
+                  <NextLink
+                    href={output.library_agent_link}
+                    className="inline-flex items-center gap-1.5"
+                    target="_blank"
+                    rel="noopener noreferrer"
+                  >
+                    <BookOpenIcon size={14} weight="regular" />
+                    Open in library
+                  </NextLink>
+                </Button>
+                <Button variant="outline" size="small">
+                  <NextLink
+                    href={output.agent_page_link}
+                    target="_blank"
+                    rel="noopener noreferrer"
+                    className="inline-flex items-center gap-1.5"
+                  >
+                    <PencilSimpleIcon size={14} weight="regular" />
+                    Open in builder
+                  </NextLink>
+                </Button>
+              </div>
+            </div>
+          )}
+
+          {output && isAgentPreviewOutput(output) && (
             <ContentGrid>
               <ContentMessage>{output.message}</ContentMessage>
               {output.description?.trim() && (
@@ -197,7 +253,7 @@ export function EditAgentTool({ part }: Props) {
             </ContentGrid>
           )}
 
-          {isClarificationNeededOutput(output) && (
+          {output && isClarificationNeededOutput(output) && (
             <ClarificationQuestionsCard
               questions={(output.questions ?? []).map((q) => {
                 const item: ClarifyingQuestion = {
@@ -215,22 +271,6 @@ export function EditAgentTool({ part }: Props) {
               onSubmitAnswers={handleClarificationAnswers}
             />
           )}
-
-          {isErrorOutput(output) && (
-            <ContentGrid>
-              <ContentMessage>{output.message}</ContentMessage>
-              {output.error && (
-                <ContentCodeBlock>
-                  {formatMaybeJson(output.error)}
-                </ContentCodeBlock>
-              )}
-              {output.details && (
-                <ContentCodeBlock>
-                  {formatMaybeJson(output.details)}
-                </ContentCodeBlock>
-              )}
-            </ContentGrid>
-          )}
         </ToolAccordion>
       )}
     </div>

autogpt_platform/frontend/src/app/(platform)/copilot/tools/EditAgent/helpers.tsx

@@ -2,9 +2,6 @@ import type { AgentPreviewResponse } from "@/app/api/__generated__/models/agentP
 import type { AgentSavedResponse } from "@/app/api/__generated__/models/agentSavedResponse";
 import type { ClarificationNeededResponse } from "@/app/api/__generated__/models/clarificationNeededResponse";
 import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import type { OperationInProgressResponse } from "@/app/api/__generated__/models/operationInProgressResponse";
-import type { OperationPendingResponse } from "@/app/api/__generated__/models/operationPendingResponse";
-import type { OperationStartedResponse } from "@/app/api/__generated__/models/operationStartedResponse";
 import { ResponseType } from "@/app/api/__generated__/models/responseType";
 import {
   NotePencilIcon,
@@ -15,9 +12,6 @@ import type { ToolUIPart } from "ai";
 import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
 
 export type EditAgentToolOutput =
-  | OperationStartedResponse
-  | OperationPendingResponse
-  | OperationInProgressResponse
   | AgentPreviewResponse
   | AgentSavedResponse
   | ClarificationNeededResponse
@@ -37,9 +31,6 @@ function parseOutput(output: unknown): EditAgentToolOutput | null {
   if (typeof output === "object") {
     const type = (output as { type?: unknown }).type;
     if (
-      type === ResponseType.operation_started ||
-      type === ResponseType.operation_pending ||
-      type === ResponseType.operation_in_progress ||
       type === ResponseType.agent_preview ||
       type === ResponseType.agent_saved ||
       type === ResponseType.clarification_needed ||
@@ -47,9 +38,6 @@ function parseOutput(output: unknown): EditAgentToolOutput | null {
     ) {
       return output as EditAgentToolOutput;
     }
-    if ("operation_id" in output && "tool_name" in output)
-      return output as OperationStartedResponse | OperationPendingResponse;
-    if ("tool_call_id" in output) return output as OperationInProgressResponse;
     if ("agent_json" in output && "agent_name" in output)
       return output as AgentPreviewResponse;
     if ("agent_id" in output && "library_agent_id" in output)
@@ -68,30 +56,6 @@ export function getEditAgentToolOutput(
   return parseOutput((part as { output?: unknown }).output);
 }
 
-export function isOperationStartedOutput(
-  output: EditAgentToolOutput,
-): output is OperationStartedResponse {
-  return (
-    output.type === ResponseType.operation_started ||
-    ("operation_id" in output && "tool_name" in output)
-  );
-}
-
-export function isOperationPendingOutput(
-  output: EditAgentToolOutput,
-): output is OperationPendingResponse {
-  return output.type === ResponseType.operation_pending;
-}
-
-export function isOperationInProgressOutput(
-  output: EditAgentToolOutput,
-): output is OperationInProgressResponse {
-  return (
-    output.type === ResponseType.operation_in_progress ||
-    "tool_call_id" in output
-  );
-}
-
 export function isAgentPreviewOutput(
   output: EditAgentToolOutput,
 ): output is AgentPreviewResponse {
@@ -132,10 +96,6 @@ export function getAnimationText(part: {
     case "output-available": {
       const output = parseOutput(part.output);
       if (!output) return "Editing the agent";
-      if (isOperationStartedOutput(output)) return "Agent update started";
-      if (isOperationPendingOutput(output)) return "Agent update in progress";
-      if (isOperationInProgressOutput(output))
-        return "Agent update already in progress";
       if (isAgentSavedOutput(output)) return `Saved "${output.agent_name}"`;
       if (isAgentPreviewOutput(output)) return `Preview "${output.agent_name}"`;
       if (isClarificationNeededOutput(output)) return "Needs clarification";

autogpt_platform/frontend/src/app/(platform)/copilot/tools/GenericTool/GenericTool.tsx

@@ -686,17 +686,20 @@ export function GenericTool({ part }: Props) {
 
   return (
     <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon
-          category={category}
-          isStreaming={isStreaming}
-          isError={isError}
-        />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
+      {/* Only show loading text when NOT showing accordion */}
+      {!showAccordion && (
+        <div className="flex items-center gap-2 text-sm text-muted-foreground">
+          <ToolIcon
+            category={category}
+            isStreaming={isStreaming}
+            isError={isError}
+          />
+          <MorphingTextAnimation
+            text={text}
+            className={isError ? "text-red-500" : undefined}
+          />
+        </div>
+      )}
 
       {showAccordion && accordionData ? (
         <ToolAccordion

autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/RunAgent.tsx

@@ -69,13 +69,20 @@ export function RunAgentTool({ part }: Props) {
 
   return (
     <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
+      {/* Only show loading text when NOT showing accordion or other content */}
+      {!isStreaming &&
+        !setupRequirementsOutput &&
+        !agentDetailsOutput &&
+        !needLoginOutput &&
+        !hasExpandableContent && (
+          <div className="flex items-center gap-2 text-sm text-muted-foreground">
+            <ToolIcon isStreaming={isStreaming} isError={isError} />
+            <MorphingTextAnimation
+              text={text}
+              className={isError ? "text-red-500" : undefined}
+            />
+          </div>
+        )}
 
       {isStreaming && !output && (
         <ToolAccordion

autogpt_platform/frontend/src/app/(platform)/copilot/useChatSession.ts

@@ -20,7 +20,8 @@ export function useChatSession() {
       enabled: !!sessionId,
       staleTime: Infinity,
       refetchOnWindowFocus: false,
-      refetchOnReconnect: false,
+      refetchOnReconnect: true,
+      refetchOnMount: true,
     },
   });
 
@@ -115,6 +116,7 @@ export function useChatSession() {
     hydratedMessages,
     hasActiveStream,
     isLoadingSession: sessionQuery.isLoading,
+    isSessionError: sessionQuery.isError,
     createSession,
     isCreatingSession,
   };

autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotPage.ts

@@ -14,7 +14,6 @@ import { DefaultChatTransport } from "ai";
 import type { UIMessage } from "ai";
 import { useCallback, useEffect, useMemo, useRef, useState } from "react";
 import { useChatSession } from "./useChatSession";
-import { useLongRunningToolPolling } from "./hooks/useLongRunningToolPolling";
 
 const STREAM_START_TIMEOUT_MS = 12_000;
 
@@ -36,6 +35,46 @@ function resolveInProgressTools(
   }));
 }
 
+/** Build a fingerprint from a message's role + text/tool content for cross-boundary dedup. */
+function messageFingerprint(msg: UIMessage): string {
+  const fragments = msg.parts.map((p) => {
+    if ("text" in p && typeof p.text === "string") return p.text;
+    if ("toolCallId" in p && typeof p.toolCallId === "string")
+      return `tool:${p.toolCallId}`;
+    return "";
+  });
+  return `${msg.role}::${fragments.join("\n")}`;
+}
+
+/**
+ * Deduplicate messages by ID *and* by content fingerprint.
+ * ID-based dedup catches duplicates within the same source (e.g. two
+ * identical stream events).  Fingerprint-based dedup catches duplicates
+ * across the hydration/stream boundary where IDs differ (synthetic
+ * `${sessionId}-${index}` vs AI SDK nanoid).
+ *
+ * NOTE: Fingerprint dedup only applies to assistant messages, not user messages.
+ * Users should be able to send the same message multiple times.
+ */
+function deduplicateMessages(messages: UIMessage[]): UIMessage[] {
+  const seenIds = new Set<string>();
+  const seenFingerprints = new Set<string>();
+  return messages.filter((msg) => {
+    if (seenIds.has(msg.id)) return false;
+    seenIds.add(msg.id);
+
+    // Only apply fingerprint deduplication to assistant messages
+    // User messages should allow duplicates (same text sent multiple times)
+    if (msg.role === "assistant") {
+      const fp = messageFingerprint(msg);
+      if (fp !== "::" && seenFingerprints.has(fp)) return false;
+      seenFingerprints.add(fp);
+    }
+
+    return true;
+  });
+}
+
 export function useCopilotPage() {
   const { isUserLoading, isLoggedIn } = useSupabase();
   const [isDrawerOpen, setIsDrawerOpen] = useState(false);
@@ -52,6 +91,7 @@ export function useCopilotPage() {
     hydratedMessages,
     hasActiveStream,
     isLoadingSession,
+    isSessionError,
     createSession,
     isCreatingSession,
   } = useChatSession();
@@ -114,7 +154,7 @@ export function useCopilotPage() {
   );
 
   const {
-    messages,
+    messages: rawMessages,
     sendMessage,
     stop: sdkStop,
     status,
@@ -129,6 +169,12 @@ export function useCopilotPage() {
     // call resumeStream() manually after hydration + active_stream detection.
   });
 
+  // Deduplicate messages continuously to prevent duplicates when resuming streams
+  const messages = useMemo(
+    () => deduplicateMessages(rawMessages),
+    [rawMessages],
+  );
+
   // Wrap AI SDK's stop() to also cancel the backend executor task.
   // sdkStop() aborts the SSE fetch instantly (UI feedback), then we fire
   // the cancel API to actually stop the executor and wait for confirmation.
@@ -184,19 +230,26 @@ export function useCopilotPage() {
     if (status === "streaming" || status === "submitted") return;
     setMessages((prev) => {
       if (prev.length >= hydratedMessages.length) return prev;
-      return hydratedMessages;
+      // Deduplicate to handle rare cases where duplicate streams might occur
+      return deduplicateMessages(hydratedMessages);
     });
   }, [hydratedMessages, setMessages, status]);
 
   // Ref: tracks whether we've already resumed for a given session.
-  // Reset when the stream ends so re-resume is possible if the backend
-  // task is still running (SSE dropped but executor didn't finish).
-  const hasResumedRef = useRef<string | null>(null);
+  // Format: Map<sessionId, hasResumed>
+  const hasResumedRef = useRef<Map<string, boolean>>(new Map());
 
   // When the stream ends (or drops), invalidate the session cache so the
   // next hydration fetches fresh messages from the backend.  Without this,
   // staleTime: Infinity means the cache keeps the pre-stream data forever,
   // and any messages added during streaming are lost on remount/navigation.
+  // Track status transitions for cache invalidation and auto-reconnect.
+  // Auto-reconnect: GCP's L7 load balancer kills SSE connections at ~5 min.
+  // When that happens the AI SDK goes "streaming" → "error".  If the backend
+  // executor is still running (hasActiveStream), we call resumeStream() to
+  // reconnect via GET and replay from Redis.
+  const MAX_RECONNECT_ATTEMPTS = 3;
+  const reconnectAttemptsRef = useRef(0);
   const prevStatusRef = useRef(status);
   useEffect(() => {
     const prev = prevStatusRef.current;
@@ -204,33 +257,63 @@ export function useCopilotPage() {
 
     const wasActive = prev === "streaming" || prev === "submitted";
     const isIdle = status === "ready" || status === "error";
+
+    // Invalidate session cache when stream ends so hydration fetches fresh data
     if (wasActive && isIdle && sessionId) {
       queryClient.invalidateQueries({
         queryKey: getGetV2GetSessionQueryKey(sessionId),
       });
-      // Allow re-resume if the backend task is still running.
-      hasResumedRef.current = null;
     }
-  }, [status, sessionId, queryClient]);
+
+    // Auto-reconnect on mid-stream SSE drop
+    if (
+      prev === "streaming" &&
+      status === "error" &&
+      sessionId &&
+      hasActiveStream
+    ) {
+      if (reconnectAttemptsRef.current < MAX_RECONNECT_ATTEMPTS) {
+        reconnectAttemptsRef.current += 1;
+        const attempt = reconnectAttemptsRef.current;
+        console.info(
+          `[copilot] SSE dropped mid-stream, reconnecting (attempt ${attempt}/${MAX_RECONNECT_ATTEMPTS})...`,
+        );
+        const timer = setTimeout(() => resumeStream(), 1_000);
+        return () => clearTimeout(timer);
+      } else {
+        toast({
+          title: "Connection lost",
+          description:
+            "Could not reconnect to the stream. Please refresh the page.",
+          variant: "destructive",
+        });
+      }
+    }
+
+    // Reset reconnect counter when stream completes normally or resumes
+    if (status === "ready" || status === "streaming") {
+      reconnectAttemptsRef.current = 0;
+    }
+  }, [status, sessionId, hasActiveStream, queryClient, resumeStream]);
 
   // Resume an active stream AFTER hydration completes.
-  // The backend returns active_stream info when a task is still running.
-  // We wait for hydration so the AI SDK has the conversation history
-  // before the resumed stream appends the in-progress assistant message.
+  // IMPORTANT: Only runs when page loads with existing active stream (reconnection).
+  // Does NOT run when new streams start during active conversation.
   useEffect(() => {
-    if (!hasActiveStream || !sessionId) return;
+    if (!sessionId) return;
+    if (!hasActiveStream) return;
     if (!hydratedMessages || hydratedMessages.length === 0) return;
-    if (status === "streaming" || status === "submitted") return;
-    // Only resume once per session to avoid re-triggering after stream ends
-    if (hasResumedRef.current === sessionId) return;
-    hasResumedRef.current = sessionId;
-    resumeStream();
-  }, [hasActiveStream, sessionId, hydratedMessages, status, resumeStream]);
 
-  // Poll session endpoint when a long-running tool (create_agent, edit_agent)
-  // is in progress. When the backend completes, the session data will contain
-  // the final tool output — this hook detects the change and updates messages.
-  useLongRunningToolPolling(sessionId, messages, setMessages);
+    // Never resume if currently streaming
+    if (status === "streaming" || status === "submitted") return;
+
+    // Only resume once per session
+    if (hasResumedRef.current.get(sessionId)) return;
+
+    // Mark as resumed immediately to prevent race conditions
+    hasResumedRef.current.set(sessionId, true);
+    resumeStream();
+  }, [sessionId, hasActiveStream, hydratedMessages, status, resumeStream]);
 
   // Clear messages when session is null
   useEffect(() => {
@@ -321,6 +404,7 @@ export function useCopilotPage() {
     stop,
     isReconnecting,
     isLoadingSession,
+    isSessionError,
     isCreatingSession,
     isUserLoading,
     isLoggedIn,

autogpt_platform/frontend/src/app/api/chat/tasks/[taskId]/stream/route.ts

@@ -1,64 +0,0 @@
-import { environment } from "@/services/environment";
-import { getServerAuthToken } from "@/lib/autogpt-server-api/helpers";
-import { NextRequest } from "next/server";
-import { normalizeSSEStream, SSE_HEADERS } from "../../../sse-helpers";
-
-export async function GET(
-  request: NextRequest,
-  { params }: { params: Promise<{ taskId: string }> },
-) {
-  const { taskId } = await params;
-  const searchParams = request.nextUrl.searchParams;
-  const lastMessageId = searchParams.get("last_message_id") || "0-0";
-
-  try {
-    const token = await getServerAuthToken();
-
-    const backendUrl = environment.getAGPTServerBaseUrl();
-    const streamUrl = new URL(`/api/chat/tasks/${taskId}/stream`, backendUrl);
-    streamUrl.searchParams.set("last_message_id", lastMessageId);
-
-    const headers: Record<string, string> = {
-      Accept: "text/event-stream",
-      "Cache-Control": "no-cache",
-      Connection: "keep-alive",
-    };
-
-    if (token) {
-      headers["Authorization"] = `Bearer ${token}`;
-    }
-
-    const response = await fetch(streamUrl.toString(), {
-      method: "GET",
-      headers,
-    });
-
-    if (!response.ok) {
-      const error = await response.text();
-      return new Response(error, {
-        status: response.status,
-        headers: { "Content-Type": "application/json" },
-      });
-    }
-
-    if (!response.body) {
-      return new Response(null, { status: 204 });
-    }
-
-    return new Response(normalizeSSEStream(response.body), {
-      headers: SSE_HEADERS,
-    });
-  } catch (error) {
-    console.error("Task stream proxy error:", error);
-    return new Response(
-      JSON.stringify({
-        error: "Failed to connect to task stream",
-        detail: error instanceof Error ? error.message : String(error),
-      }),
-      {
-        status: 500,
-        headers: { "Content-Type": "application/json" },
-      },
-    );
-  }
-}

autogpt_platform/frontend/src/app/api/openapi.json

@@ -961,63 +961,6 @@
         }
       }
     },
-    "/api/chat/operations/{operation_id}/complete": {
-      "post": {
-        "tags": ["v2", "chat", "chat"],
-        "summary": "Complete Operation",
-        "description": "External completion webhook for long-running operations.\n\nCalled by Agent Generator (or other services) when an operation completes.\nThis triggers the stream registry to publish completion and continue LLM generation.\n\nArgs:\n    operation_id: The operation ID to complete.\n    request: Completion payload with success status and result/error.\n    x_api_key: Internal API key for authentication.\n\nReturns:\n    dict: Status of the completion.\n\nRaises:\n    HTTPException: If API key is invalid or operation not found.",
-        "operationId": "postV2CompleteOperation",
-        "parameters": [
-          {
-            "name": "operation_id",
-            "in": "path",
-            "required": true,
-            "schema": { "type": "string", "title": "Operation Id" }
-          },
-          {
-            "name": "x-api-key",
-            "in": "header",
-            "required": false,
-            "schema": {
-              "anyOf": [{ "type": "string" }, { "type": "null" }],
-              "title": "X-Api-Key"
-            }
-          }
-        ],
-        "requestBody": {
-          "required": true,
-          "content": {
-            "application/json": {
-              "schema": {
-                "$ref": "#/components/schemas/OperationCompleteRequest"
-              }
-            }
-          }
-        },
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "type": "object",
-                  "additionalProperties": true,
-                  "title": "Response Postv2Completeoperation"
-                }
-              }
-            }
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
-              }
-            }
-          }
-        }
-      }
-    },
     "/api/chat/schema/tool-responses": {
       "get": {
         "tags": ["v2", "chat", "chat"],
@@ -1057,12 +1000,7 @@
                     { "$ref": "#/components/schemas/BlockDetailsResponse" },
                     { "$ref": "#/components/schemas/BlockOutputResponse" },
                     { "$ref": "#/components/schemas/DocSearchResultsResponse" },
-                    { "$ref": "#/components/schemas/DocPageResponse" },
-                    { "$ref": "#/components/schemas/OperationStartedResponse" },
-                    { "$ref": "#/components/schemas/OperationPendingResponse" },
-                    {
-                      "$ref": "#/components/schemas/OperationInProgressResponse"
-                    }
+                    { "$ref": "#/components/schemas/DocPageResponse" }
                   ],
                   "title": "Response Getv2[Dummy] Tool Response Type Export For Codegen"
                 }
@@ -1185,7 +1123,7 @@
       "get": {
         "tags": ["v2", "chat", "chat"],
         "summary": "Get Session",
-        "description": "Retrieve the details of a specific chat session.\n\nLooks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.\nIf there's an active stream for this session, returns the task_id for reconnection.\n\nArgs:\n    session_id: The unique identifier for the desired chat session.\n    user_id: The optional authenticated user ID, or None for anonymous access.\n\nReturns:\n    SessionDetailResponse: Details for the requested session, including active_stream info if applicable.",
+        "description": "Retrieve the details of a specific chat session.\n\nLooks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.\nIf there's an active stream for this session, returns active_stream info for reconnection.\n\nArgs:\n    session_id: The unique identifier for the desired chat session.\n    user_id: The optional authenticated user ID, or None for anonymous access.\n\nReturns:\n    SessionDetailResponse: Details for the requested session, including active_stream info if applicable.",
         "operationId": "getV2GetSession",
         "security": [{ "HTTPBearerJWT": [] }],
         "parameters": [
@@ -1283,7 +1221,9 @@
             "description": "Successful Response",
             "content": {
               "application/json": {
-                "schema": { "$ref": "#/components/schemas/CancelTaskResponse" }
+                "schema": {
+                  "$ref": "#/components/schemas/CancelSessionResponse"
+                }
               }
             }
           },
@@ -1337,7 +1277,7 @@
       "post": {
         "tags": ["v2", "chat", "chat"],
         "summary": "Stream Chat Post",
-        "description": "Stream chat responses for a session (POST with context support).\n\nStreams the AI/completion responses in real time over Server-Sent Events (SSE), including:\n  - Text fragments as they are generated\n  - Tool call UI elements (if invoked)\n  - Tool execution results\n\nThe AI generation runs in a background task that continues even if the client disconnects.\nAll chunks are written to Redis for reconnection support. If the client disconnects,\nthey can reconnect using GET /tasks/{task_id}/stream to resume from where they left off.\n\nArgs:\n    session_id: The chat session identifier to associate with the streamed messages.\n    request: Request body containing message, is_user_message, and optional context.\n    user_id: Optional authenticated user ID.\nReturns:\n    StreamingResponse: SSE-formatted response chunks. First chunk is a \"start\" event\n    containing the task_id for reconnection.",
+        "description": "Stream chat responses for a session (POST with context support).\n\nStreams the AI/completion responses in real time over Server-Sent Events (SSE), including:\n  - Text fragments as they are generated\n  - Tool call UI elements (if invoked)\n  - Tool execution results\n\nThe AI generation runs in a background task that continues even if the client disconnects.\nAll chunks are written to a per-turn Redis stream for reconnection support. If the client\ndisconnects, they can reconnect using GET /sessions/{session_id}/stream to resume.\n\nArgs:\n    session_id: The chat session identifier to associate with the streamed messages.\n    request: Request body containing message, is_user_message, and optional context.\n    user_id: Optional authenticated user ID.\nReturns:\n    StreamingResponse: SSE-formatted response chunks.",
         "operationId": "postV2StreamChatPost",
         "security": [{ "HTTPBearerJWT": [] }],
         "parameters": [
@@ -1375,94 +1315,6 @@
         }
       }
     },
-    "/api/chat/tasks/{task_id}": {
-      "get": {
-        "tags": ["v2", "chat", "chat"],
-        "summary": "Get Task Status",
-        "description": "Get the status of a long-running task.\n\nArgs:\n    task_id: The task ID to check.\n    user_id: Authenticated user ID for ownership validation.\n\nReturns:\n    dict: Task status including task_id, status, tool_name, and operation_id.\n\nRaises:\n    NotFoundError: If task_id is not found or user doesn't have access.",
-        "operationId": "getV2GetTaskStatus",
-        "security": [{ "HTTPBearerJWT": [] }],
-        "parameters": [
-          {
-            "name": "task_id",
-            "in": "path",
-            "required": true,
-            "schema": { "type": "string", "title": "Task Id" }
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "type": "object",
-                  "additionalProperties": true,
-                  "title": "Response Getv2Gettaskstatus"
-                }
-              }
-            }
-          },
-          "401": {
-            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
-              }
-            }
-          }
-        }
-      }
-    },
-    "/api/chat/tasks/{task_id}/stream": {
-      "get": {
-        "tags": ["v2", "chat", "chat"],
-        "summary": "Stream Task",
-        "description": "Reconnect to a long-running task's SSE stream.\n\nWhen a long-running operation (like agent generation) starts, the client\nreceives a task_id. If the connection drops, the client can reconnect\nusing this endpoint to resume receiving updates.\n\nArgs:\n    task_id: The task ID from the operation_started response.\n    user_id: Authenticated user ID for ownership validation.\n    last_message_id: Last Redis Stream message ID received (\"0-0\" for full replay).\n\nReturns:\n    StreamingResponse: SSE-formatted response chunks starting after last_message_id.\n\nRaises:\n    HTTPException: 404 if task not found, 410 if task expired, 403 if access denied.",
-        "operationId": "getV2StreamTask",
-        "security": [{ "HTTPBearerJWT": [] }],
-        "parameters": [
-          {
-            "name": "task_id",
-            "in": "path",
-            "required": true,
-            "schema": { "type": "string", "title": "Task Id" }
-          },
-          {
-            "name": "last_message_id",
-            "in": "query",
-            "required": false,
-            "schema": {
-              "type": "string",
-              "description": "Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay.",
-              "default": "0-0",
-              "title": "Last Message Id"
-            },
-            "description": "Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay."
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": { "application/json": { "schema": {} } }
-          },
-          "401": {
-            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
-              }
-            }
-          }
-        }
-      }
-    },
     "/api/credits": {
       "get": {
         "tags": ["v1", "credits"],
@@ -6562,13 +6414,11 @@
       },
       "ActiveStreamInfo": {
         "properties": {
-          "task_id": { "type": "string", "title": "Task Id" },
-          "last_message_id": { "type": "string", "title": "Last Message Id" },
-          "operation_id": { "type": "string", "title": "Operation Id" },
-          "tool_name": { "type": "string", "title": "Tool Name" }
+          "turn_id": { "type": "string", "title": "Turn Id" },
+          "last_message_id": { "type": "string", "title": "Last Message Id" }
         },
         "type": "object",
-        "required": ["task_id", "last_message_id", "operation_id", "tool_name"],
+        "required": ["turn_id", "last_message_id"],
         "title": "ActiveStreamInfo",
         "description": "Information about an active stream for reconnection."
       },
@@ -7575,13 +7425,9 @@
         "required": ["file"],
         "title": "Body_postV2Upload submission media"
       },
-      "CancelTaskResponse": {
+      "CancelSessionResponse": {
         "properties": {
           "cancelled": { "type": "boolean", "title": "Cancelled" },
-          "task_id": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Task Id"
-          },
           "reason": {
             "anyOf": [{ "type": "string" }, { "type": "null" }],
             "title": "Reason"
@@ -7589,8 +7435,8 @@
         },
         "type": "object",
         "required": ["cancelled"],
-        "title": "CancelTaskResponse",
-        "description": "Response model for the cancel task endpoint."
+        "title": "CancelSessionResponse",
+        "description": "Response model for the cancel session endpoint."
       },
       "ChangelogEntry": {
         "properties": {
@@ -10107,87 +9953,6 @@
         ],
         "title": "OnboardingStep"
       },
-      "OperationCompleteRequest": {
-        "properties": {
-          "success": { "type": "boolean", "title": "Success" },
-          "result": {
-            "anyOf": [
-              { "additionalProperties": true, "type": "object" },
-              { "type": "string" },
-              { "type": "null" }
-            ],
-            "title": "Result"
-          },
-          "error": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Error"
-          }
-        },
-        "type": "object",
-        "required": ["success"],
-        "title": "OperationCompleteRequest",
-        "description": "Request model for external completion webhook."
-      },
-      "OperationInProgressResponse": {
-        "properties": {
-          "type": {
-            "$ref": "#/components/schemas/ResponseType",
-            "default": "operation_in_progress"
-          },
-          "message": { "type": "string", "title": "Message" },
-          "session_id": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Session Id"
-          },
-          "tool_call_id": { "type": "string", "title": "Tool Call Id" }
-        },
-        "type": "object",
-        "required": ["message", "tool_call_id"],
-        "title": "OperationInProgressResponse",
-        "description": "Response when an operation is already in progress.\n\nReturned for idempotency when the same tool_call_id is requested again\nwhile the background task is still running."
-      },
-      "OperationPendingResponse": {
-        "properties": {
-          "type": {
-            "$ref": "#/components/schemas/ResponseType",
-            "default": "operation_pending"
-          },
-          "message": { "type": "string", "title": "Message" },
-          "session_id": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Session Id"
-          },
-          "operation_id": { "type": "string", "title": "Operation Id" },
-          "tool_name": { "type": "string", "title": "Tool Name" }
-        },
-        "type": "object",
-        "required": ["message", "operation_id", "tool_name"],
-        "title": "OperationPendingResponse",
-        "description": "Response stored in chat history while a long-running operation is executing.\n\nThis is persisted to the database so users see a pending state when they\nrefresh before the operation completes."
-      },
-      "OperationStartedResponse": {
-        "properties": {
-          "type": {
-            "$ref": "#/components/schemas/ResponseType",
-            "default": "operation_started"
-          },
-          "message": { "type": "string", "title": "Message" },
-          "session_id": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Session Id"
-          },
-          "operation_id": { "type": "string", "title": "Operation Id" },
-          "tool_name": { "type": "string", "title": "Tool Name" },
-          "task_id": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Task Id"
-          }
-        },
-        "type": "object",
-        "required": ["message", "operation_id", "tool_name"],
-        "title": "OperationStartedResponse",
-        "description": "Response when a long-running operation has been started in the background.\n\nThis is returned immediately to the client while the operation continues\nto execute. The user can close the tab and check back later.\n\nThe task_id can be used to reconnect to the SSE stream via\nGET /chat/tasks/{task_id}/stream?last_idx=0"
-      },
       "Pagination": {
         "properties": {
           "total_items": {
@@ -10844,13 +10609,10 @@
           "workspace_file_metadata",
           "workspace_file_written",
           "workspace_file_deleted",
-          "operation_started",
-          "operation_pending",
           "operation_in_progress",
           "input_validation_error",
           "web_fetch",
           "bash_exec",
-          "operation_status",
           "feature_request_search",
           "feature_request_created",
           "suggested_goal"

docs/platform/getting-started.md

@@ -218,6 +218,17 @@ If you initially installed Docker with Hyper-V, you **don’t need to reinstall*
 
 For more details, refer to [Docker's official documentation](https://docs.docker.com/desktop/windows/wsl/).
 
+### ⚠️ Podman Not Supported
+
+AutoGPT requires **Docker** (Docker Desktop or Docker Engine). **Podman and podman-compose are not supported** and may cause path resolution issues, particularly on Windows.
+
+If you see errors like:
+```text
+Error: the specified Containerfile or Dockerfile does not exist, ..\..\autogpt_platform\backend\Dockerfile
+```
+
+This indicates you're using Podman instead of Docker. Please install [Docker Desktop](https://docs.docker.com/desktop/) and use `docker compose` instead of `podman-compose`.
+
 
 ## Development